Final report on link level and system level channel models - Winner
Final report on link level and system level channel models - Winner
Final report on link level and system level channel models - Winner
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WINNER D5.4 v. 1.4<br />
IST-2003-507581 WINNER<br />
D5.4 v. 1.4<br />
<str<strong>on</strong>g>Final</str<strong>on</strong>g> Report <strong>on</strong> Link Level <strong>and</strong> System Level Channel Models<br />
Date of Delivery to the CEC: Nov. 18 th , 2005<br />
Author(s):<br />
Participant(s):<br />
Workpackage:<br />
Daniel S. Baum, Hassan El-Sallabi, Tommi Jämsä, Juha Meinilä, Pekka<br />
Kyösti, Xi<strong>on</strong>gwen Zhao, Daniela Laselva, Jukka-Pekka Nuutinen, Lassi<br />
Hentilä, Pertti Vainikainen, Jarmo Kivinen, Lasse Vuokko, Per<br />
Zetterberg, Mats Bengtss<strong>on</strong>, Kai Yu, Niklas Jaldén, Terhi Rautiainen,<br />
Kimmo Kalliola, Marko Milojevic, Christian Schneider, Jan Hansen.<br />
EBIT, EBITT, ETHZ, HUT, KTH, NOK, TUI<br />
WP5 – Channel Modelling<br />
Estimated pers<strong>on</strong> m<strong>on</strong>ths: 66<br />
Security:<br />
Public<br />
Nature:<br />
R<br />
Versi<strong>on</strong>: 1.4<br />
Total number of pages: 167<br />
Abstract: This document presents WINNER <strong>channel</strong> <strong>models</strong>. The <strong>channel</strong> <strong>models</strong> cover WINNER<br />
propagati<strong>on</strong> scenarios for indoor, urban macro-cell <strong>and</strong> micro-cell, stati<strong>on</strong>ary feeder, suburban macro-cell,<br />
<strong>and</strong> rural macro-cell. Both geometric-based stochastic <strong>channel</strong> model <strong>and</strong> reduced-variability (clustered<br />
delay-line) <strong>models</strong> are presented. The <strong>channel</strong> <strong>models</strong> are mainly based <strong>on</strong> measurement data.<br />
Keyword list: Channel modelling, propagati<strong>on</strong> scenarios, wideb<strong>and</strong>, <strong>channel</strong> sounder, cluster delay<br />
domain, angle domain, measurements, delay spread, ray, angle-spread, arrival, departure<br />
Disclaimer:<br />
Page 1 (167)
WINNER D5.4 v. 1.4<br />
Executive Summary<br />
This deliverable presents WINNER <strong>channel</strong> <strong>models</strong> for <strong>link</strong> <strong>level</strong> <strong>and</strong> <strong>system</strong> <strong>level</strong> simulati<strong>on</strong>s of short<br />
range <strong>and</strong> wide area wireless communicati<strong>on</strong> <strong>system</strong>s. The developed <strong>channel</strong> <strong>models</strong> follow guidelines<br />
stated in WINNER deliverable D5.2. The <strong>models</strong> are antenna independent, i.e., different antenna<br />
c<strong>on</strong>figurati<strong>on</strong>s <strong>and</strong> different element patterns can be inserted. The covered propagati<strong>on</strong> scenarios are<br />
indoor small office, urban micro-cell, indoor, stati<strong>on</strong>ary feeder, suburban macro-cell, urban macro-cell,<br />
<strong>and</strong> rural macro-cell. The generic WINNER <strong>channel</strong> model follows a geometric-based stochastic <strong>channel</strong><br />
modelling approach, which allows creating of virtually unlimited double directi<strong>on</strong>al radio <strong>channel</strong> model.<br />
Clustered delay line <strong>models</strong> have also been created for calibrati<strong>on</strong> <strong>and</strong> comparis<strong>on</strong> of different<br />
simulati<strong>on</strong>s. The developed <strong>models</strong> are based <strong>on</strong> both literature <strong>and</strong> extensive measurement campaigns<br />
that have been carried out within the WINNER project.<br />
Page 2 (167)
WINNER D5.4 v. 1.4<br />
Authors<br />
Partner Name Ph<strong>on</strong>e / Fax / e-mail<br />
ETHZ Daniel S. Baum Ph<strong>on</strong>e: +41 44 632 2791<br />
Fax: +41 44 632 1209<br />
E-mail: dsbaum@nari.ee.ethz.ch<br />
HUT Hassan El-Sallabi Ph<strong>on</strong>e: +358 9 451 5960<br />
Fax: +358 9 451 2152<br />
E-mail: hsallabi@cc.hut.fi<br />
EBIT Tommi Jämsä Ph<strong>on</strong>e: +358 40 344 2000<br />
Fax: +358 8 551 4344<br />
E-mail: tommi.jamsa@elektrobit.com<br />
EBIT Juha Meinilä Ph<strong>on</strong>e: +358 40 344 2000<br />
Fax: +358 8 551 4344<br />
E-mail: juha.meinila@elektrobit.com<br />
EBIT Pekka Kyösti Ph<strong>on</strong>e: +358 40 344 2000<br />
Fax: +358 8 551 4344<br />
E-mail: pekka.kyosti@elektrobit.com<br />
EBIT Xi<strong>on</strong>gwen Zhao Ph<strong>on</strong>e: +358 40 344 2000<br />
Fax: +358 9 5121233<br />
E-mail: xi<strong>on</strong>gwen.zhao@elektrobit.com<br />
EBIT Daniela Laselva Ph<strong>on</strong>e: +358 40 344 2000<br />
Fax: +358 8 551 4344<br />
E-mail: daniela.laselva@elektrobit.com<br />
EBIT Jukka-Pekka Nuutinen Ph<strong>on</strong>e: +358 40 344 2000<br />
Fax: +358 8 551 4344<br />
E-mail: jukka-pekka.nuutinen@elektrobit.com<br />
Page 3 (167)
WINNER D5.4 v. 1.4<br />
Partner Name Ph<strong>on</strong>e / Fax / e-mail<br />
EBIT Lassi Hentilä Ph<strong>on</strong>e: +358 40 344 2000<br />
Fax: +358 8 551 4344<br />
E-mail: lassi.hentila@elektrobit.com<br />
HUT Pertti Vainikainen Ph<strong>on</strong>e: +358 9 451 2251<br />
Fax: +358 9 451 2152<br />
E-mail: pertti.vainikainen@tkk.fi<br />
HUT Jarmo Kivinen Ph<strong>on</strong>e: +358 9 451 2242<br />
Fax: +358 9 451 2152<br />
E-mail: jarmo.kivinen@tkk.fi<br />
HUT Lasse Vuokko Ph<strong>on</strong>e: +358 9 451 6064<br />
Fax: +358 9 451 2152<br />
E-mail: lasse.vuokko@tkk.fi<br />
KTH Per Zetterberg Ph<strong>on</strong>e: +46 8 7907785<br />
Fax:<br />
E-mail: per.zetterberg@s3.kth.se<br />
KTH Mats Bengtss<strong>on</strong> Ph<strong>on</strong>e: +46 8 7908463<br />
Fax:<br />
E-mail: mats.bengtss<strong>on</strong>@s3.kth.se<br />
KTH Niklas Jaldén Ph<strong>on</strong>e: +46 8 7908415<br />
Fax:<br />
E-mail: niklasj@s3.kth.se<br />
NOK Terhi Rautiainen Ph<strong>on</strong>e: +358 50 4837218<br />
Fax: +358 7180 36857<br />
E-mail: terhi.rautiainen@nokia.com<br />
Page 4 (167)
WINNER D5.4 v. 1.4<br />
Partner Name Ph<strong>on</strong>e / Fax / e-mail<br />
NOK Kimmo Kalliola Ph<strong>on</strong>e: +358 50 4837226<br />
Fax: +358 7180 36857<br />
E-mail: kimmo.kalliola@nokia.com<br />
TUI Marko Milojevic Ph<strong>on</strong>e: +49 3677 69 2615<br />
Fax: +49 3677 69 1195<br />
E-mail: marko.milojevic@tu-ilmenau.de<br />
TUI Christian Schneider Ph<strong>on</strong>e: +49 3677 69 1157<br />
Fax: +49 3677 69 1113<br />
E-mail: christian.schneider@tu-ilmenau.de<br />
ETHZ Jan Hansen Ph<strong>on</strong>e : +41 44 632 0290<br />
Fax: +41 44 632 1209<br />
E-mail: hansen@nari.ee.ethz.ch<br />
Page 5 (167)
WINNER D5.4 v. 1.4<br />
Table of C<strong>on</strong>tents<br />
Part I................................................................................................................. 11<br />
1. Introducti<strong>on</strong> ............................................................................................... 12<br />
2. WINNER Scenarios.................................................................................... 14<br />
2.1 Scenario definiti<strong>on</strong>s.............................................................................................................. 14<br />
2.1.1 Scenario A1: Indoor small office .................................................................................. 14<br />
2.1.2 Scenario B1: Urban micro-cell ..................................................................................... 15<br />
2.1.3 Scenario B3: Indoor hotspot ......................................................................................... 15<br />
2.1.4 Scenario B5: Stati<strong>on</strong>ary feeder ..................................................................................... 15<br />
2.1.5 Scenario C1: Suburban macro-cell................................................................................ 16<br />
2.1.6 Scenario C2: Urban macro-cell..................................................................................... 16<br />
2.1.7 Scenario D1: Rural macro-cell...................................................................................... 17<br />
3. WINNER Channel Models ......................................................................... 17<br />
3.1 Generic model...................................................................................................................... 17<br />
3.1.1 Large-scale parameters................................................................................................. 17<br />
3.1.2 Average power of ZDSC c<strong>on</strong>diti<strong>on</strong>ed <strong>on</strong> their delays .................................................... 22<br />
3.1.3 Directi<strong>on</strong>al distributi<strong>on</strong>s of ZDSCs............................................................................... 23<br />
3.1.4 Antenna gain................................................................................................................ 25<br />
3.1.5 Path-loss <strong>models</strong>.......................................................................................................... 26<br />
3.1.6 Probability of line of sight............................................................................................ 26<br />
3.1.7 Generati<strong>on</strong> of <strong>channel</strong> coefficients................................................................................ 27<br />
3.2 Reduced variability “clustered delay line” model .................................................................. 29<br />
3.2.1 Scenario A1................................................................................................................. 30<br />
3.2.2 Scenario B1 ................................................................................................................. 31<br />
3.2.3 Scenario B3 ................................................................................................................. 32<br />
3.2.4 Scenario B5 ................................................................................................................. 33<br />
3.2.5 Scenario C1 ................................................................................................................. 37<br />
3.2.6 Scenario C2 ................................................................................................................. 38<br />
3.2.7 Scenario D1................................................................................................................. 39<br />
Part II................................................................................................................ 41<br />
4. Modelling Approaches.............................................................................. 42<br />
4.1 Generic <strong>channel</strong> modelling approach .................................................................................... 42<br />
4.1.1 Distincti<strong>on</strong> between <strong>channel</strong> <strong>models</strong> for <strong>link</strong>-<strong>level</strong> <strong>and</strong> <strong>system</strong>-<strong>level</strong> simulati<strong>on</strong>............. 42<br />
4.1.2 Comparis<strong>on</strong> between deterministic <strong>and</strong> stochastic <strong>channel</strong> modeling............................. 42<br />
4.1.3 Interference modeling .................................................................................................. 43<br />
4.1.4 Framework .................................................................................................................. 43<br />
4.2 Stati<strong>on</strong>ary-feeder scenarios B5 ............................................................................................. 51<br />
4.2.1 B5a LOS stati<strong>on</strong>ary feeder: rooftop-to-rooftop.............................................................. 51<br />
4.2.2 B5b LOS stati<strong>on</strong>ary feeder: street-<strong>level</strong> to street-<strong>level</strong>................................................... 52<br />
4.2.3 B5c hotspot LOS stati<strong>on</strong>ary-feeder: below rooftop to street-<strong>level</strong>.................................. 52<br />
4.2.4 B5d hotspot NLOS stati<strong>on</strong>ary feeder: rooftop to street-<strong>level</strong>.......................................... 52<br />
4.3 Coefficient generati<strong>on</strong> approaches........................................................................................ 53<br />
4.3.1 Stati<strong>on</strong>ary stochastic .................................................................................................... 53<br />
Page 6 (167)
WINNER D5.4 v. 1.4<br />
4.3.2 Sum-of-Sinusoids......................................................................................................... 54<br />
4.3.3 Problem details ............................................................................................................ 54<br />
4.3.4 Comparis<strong>on</strong> ................................................................................................................. 55<br />
4.3.5 Kr<strong>on</strong>ecker correlati<strong>on</strong>................................................................................................... 55<br />
5. Measurements <strong>and</strong> Literature Review ..................................................... 56<br />
5.1 Measurement <strong>system</strong>s .......................................................................................................... 56<br />
5.1.1 Principle of <strong>channel</strong> sounding....................................................................................... 56<br />
5.1.2 Channel sounders employed ......................................................................................... 56<br />
5.2 Measurement campaigns ...................................................................................................... 61<br />
5.2.1 Scenario A1................................................................................................................. 61<br />
5.2.2 Scenario B1 ................................................................................................................. 62<br />
5.2.3 Scenario B3 ................................................................................................................. 62<br />
5.2.4 Scenario C1 ................................................................................................................. 63<br />
5.2.5 Scenario C2 ................................................................................................................. 63<br />
5.2.6 Scenario D1................................................................................................................. 64<br />
5.2.7 Measurement summary ................................................................................................ 65<br />
5.3 Descripti<strong>on</strong> of key references ............................................................................................... 67<br />
5.4 Results of analysis items ...................................................................................................... 67<br />
5.4.1 Path-loss <strong>and</strong> shadow fading ........................................................................................ 67<br />
5.4.2 LOS probability ........................................................................................................... 73<br />
5.4.3 DS <strong>and</strong> maximum excess-delay distributi<strong>on</strong>.................................................................. 74<br />
5.4.4 Azimuth AS at BS <strong>and</strong> MS........................................................................................... 79<br />
5.4.5 Distributi<strong>on</strong> of the azimuth angles of the multipath comp<strong>on</strong>ents.................................... 83<br />
5.4.6 Angle proporti<strong>on</strong>ality factor ......................................................................................... 85<br />
5.4.7 Modelling of PDP ........................................................................................................ 87<br />
5.4.8 Number of ZDSC......................................................................................................... 91<br />
5.4.9 Distributi<strong>on</strong> of ZDSC delays ........................................................................................ 93<br />
5.4.10 Delay proporti<strong>on</strong>ality factor ......................................................................................... 96<br />
5.4.11 Ricean K-factor............................................................................................................ 98<br />
5.4.12 Cross-polarizati<strong>on</strong> ratio (XPR) ................................................................................... 101<br />
5.4.13 Large-scale parameter analysis item ........................................................................... 105<br />
5.5 Literature review................................................................................................................ 111<br />
5.5.1 Scenario A1............................................................................................................... 111<br />
5.5.2 Scenario B3 ............................................................................................................... 115<br />
5.5.3 Scenario B5 ............................................................................................................... 116<br />
5.5.4 Scenario C1 ............................................................................................................... 120<br />
5.5.5 Scenario C2 ............................................................................................................... 122<br />
5.5.6 Scenario D1............................................................................................................... 125<br />
5.6 Interpretati<strong>on</strong> of results ...................................................................................................... 127<br />
5.6.1 Path-loss.................................................................................................................... 127<br />
5.6.2 Power-delay profile.................................................................................................... 131<br />
5.6.3 Delay spread.............................................................................................................. 131<br />
5.6.4 K-factor ..................................................................................................................... 132<br />
5.6.5 Cross-polarizati<strong>on</strong> discriminati<strong>on</strong> (XPR) .................................................................... 132<br />
5.6.6 Doppler ..................................................................................................................... 132<br />
5.6.7 Angle-spread.............................................................................................................. 132<br />
Page 7 (167)
WINNER D5.4 v. 1.4<br />
5.6.8 Antenna gain.............................................................................................................. 132<br />
5.6.9 Frequency dependence of the propagati<strong>on</strong> parameters................................................. 133<br />
6. Channel Model Implementati<strong>on</strong> ............................................................. 135<br />
6.1 Overview for implementing the model................................................................................ 135<br />
6.1.1 Time sampling <strong>and</strong> interpolati<strong>on</strong> ................................................................................ 135<br />
6.1.2 Coordinate <strong>system</strong>...................................................................................................... 135<br />
6.1.3 Generati<strong>on</strong> of correlated large-scale parameters.......................................................... 137<br />
6.2 Interfaces ........................................................................................................................... 138<br />
6.2.1 Example input parameters.......................................................................................... 138<br />
6.2.2 Example output parameters ........................................................................................ 141<br />
6.3 Guidelines <strong>and</strong> examples <strong>on</strong> performing <strong>system</strong>-<strong>level</strong> simulati<strong>on</strong>s....................................... 142<br />
6.3.1 H<strong>and</strong>over ................................................................................................................... 142<br />
6.3.2 Interference................................................................................................................ 143<br />
6.3.3 Multi-cell <strong>and</strong> multi-user............................................................................................ 143<br />
6.3.4 Multihop <strong>and</strong> relaying................................................................................................ 144<br />
7. Test <strong>and</strong> Verificati<strong>on</strong> of the Channel Model <strong>and</strong> Its Implementati<strong>on</strong> .. 145<br />
7.1 Test cases........................................................................................................................... 145<br />
7.1.1 General test cases....................................................................................................... 145<br />
7.1.2 Input/output parameters.............................................................................................. 145<br />
7.1.3 Validati<strong>on</strong> of computati<strong>on</strong>.......................................................................................... 146<br />
8. References............................................................................................... 148<br />
9. Appendix.................................................................................................. 153<br />
9.1 Other scenarios .................................................................................................................. 153<br />
9.1.1 Scenario definiti<strong>on</strong>s.................................................................................................... 153<br />
9.2 Measurement campaigns for other scenarios ....................................................................... 153<br />
9.2.1 Scenario “high mobility short range hot spot”............................................................. 153<br />
9.2.2 Urban ad-hoc peer-to-peer.......................................................................................... 154<br />
9.3 Measurement results for other scenarios ............................................................................. 154<br />
9.3.1 Scenario C2: typical urban macro-cell - KTH campaign.............................................. 154<br />
9.3.2 Scenario “high mobility short range hot spot”............................................................. 156<br />
9.4 Literature review for other scenarios................................................................................... 167<br />
9.4.1 Scenario “high mobility short range hot spot”............................................................. 167<br />
Page 8 (167)
WINNER D5.4 v. 1.4<br />
List of Acr<strong>on</strong>yms <strong>and</strong> Abbreviati<strong>on</strong>s<br />
3GPP<br />
3 rd Generati<strong>on</strong> Partnership Project<br />
3GPP2 3 rd Generati<strong>on</strong> Partnership Project 2<br />
ACF<br />
ADC<br />
AoA<br />
AoD<br />
APP<br />
APS<br />
AS<br />
AWGN<br />
B3G<br />
BER<br />
BRAN<br />
BS<br />
BW<br />
C/I<br />
CDL<br />
CW<br />
D 3 SF<br />
DoA<br />
DoD<br />
DS<br />
EBIT<br />
EBITT<br />
ESPRIT<br />
ETHZ<br />
ETSI<br />
FDD<br />
FIR<br />
FS<br />
GPS<br />
HIPERLAN<br />
HUT<br />
IR<br />
ISIS<br />
KTH<br />
LNS<br />
LOS<br />
MCSSS<br />
METRA<br />
MIMO<br />
MPC<br />
MS<br />
MUSIC<br />
Auto-Correlati<strong>on</strong> Functi<strong>on</strong><br />
Analog-to-Digital C<strong>on</strong>verter<br />
Angle of Arrival<br />
Angle of Departure<br />
A Posteriori Probability<br />
Angle Power Spectrum<br />
Azimuth Spread<br />
Additive White Gaussian Noise<br />
Bey<strong>on</strong>d 3G<br />
Bit Error Rate<br />
Broadb<strong>and</strong> Radio Access Networks<br />
Base Stati<strong>on</strong><br />
B<strong>and</strong>width<br />
Carrier to Interference ratio<br />
Clustered Delay Line<br />
C<strong>on</strong>tinuous Wave<br />
Double-Directi<strong>on</strong>al Delay-Spread Functi<strong>on</strong><br />
Directi<strong>on</strong> of Arrival<br />
Directi<strong>on</strong> of Departure<br />
Delay Spread<br />
Elektrobit Ltd<br />
Elektrobit Testing Ltd<br />
Estimati<strong>on</strong> of Signal Parameters via Rotati<strong>on</strong>al Invariance Techniques<br />
Eidgenössische Technische Hochschule Zürich (Swiss Federal Institute of Technology<br />
Zurich)<br />
European Telecommunicati<strong>on</strong>s St<strong>and</strong>ards Institute<br />
Frequency Divisi<strong>on</strong> Duplex<br />
Finite Impulse Resp<strong>on</strong>se<br />
Fixed Stati<strong>on</strong><br />
Global Positi<strong>on</strong>ing System<br />
High Performance Local Area Network<br />
Helsinki University of Technology (TKK)<br />
Impulse Resp<strong>on</strong>se<br />
Initializati<strong>on</strong> <strong>and</strong> Search Improved SAGE<br />
Kungliga Tekniska Högskolan (Royal Institute of Technology in Stockholm)<br />
Log-Normal Shadowing<br />
Line-of-Sight<br />
Multi-Carrier Spread Spectrum Signal<br />
Multi-Element Transmit <strong>and</strong> Receive Antennas (European IST project)<br />
Multiple-Input Multiple-Output<br />
Multi-Path Comp<strong>on</strong>ent<br />
Mobile Stati<strong>on</strong><br />
Multiple Signal Classificati<strong>on</strong><br />
Page 9 (167)
WINNER D5.4 v. 1.4<br />
NACM<br />
NLOS<br />
NOK<br />
OFDM<br />
OLOS<br />
PAS<br />
PD 3 S<br />
PDP<br />
RMS<br />
PN<br />
RIMAX<br />
RF<br />
RX<br />
SAGE<br />
SCM<br />
SCME<br />
SF<br />
SIMO<br />
SoS<br />
SW<br />
TDL<br />
TUI<br />
TX<br />
WINNER<br />
WPx<br />
XPR<br />
XPR H<br />
XPR V<br />
ZDSC<br />
ZDSC_A<br />
ZDSC_D<br />
No Auto-Correlati<strong>on</strong> Mode<br />
N<strong>on</strong> Line-of-Sight<br />
Nokia<br />
Orthog<strong>on</strong>al Frequency-Divisi<strong>on</strong> Multiplexing<br />
Obstructed Line-of-Sight<br />
Power Azimuth Spectrum<br />
Power Double-Directi<strong>on</strong>al Delay-Spectrum<br />
Power-Delay Profile<br />
Root Mean Square<br />
Pseudo Noise<br />
maximum likelihood parameter estimati<strong>on</strong> framework for joint superresoluti<strong>on</strong> estimati<strong>on</strong><br />
of both specular <strong>and</strong> dense multipath comp<strong>on</strong>ents<br />
Radio Frequency<br />
Receiver<br />
Space-Alternating Generalized Expectati<strong>on</strong>-maximizati<strong>on</strong><br />
Spatial Channel Model<br />
Spatial Channel Model Extended<br />
Shadow Fading<br />
Single-Input Multiple-Output<br />
Sum of Sinusoids<br />
Software<br />
Tapped Delay-Line<br />
Technische Universität Ilmenau<br />
Transmitter<br />
Wireless World Initiative New Radio<br />
Work Package x of WINNER project<br />
Cross-Polarisati<strong>on</strong> Ratio<br />
Horiz<strong>on</strong>tal Polarisati<strong>on</strong> XPR<br />
Vertical Polarisati<strong>on</strong> XPR<br />
Zero Delay-Spread Cluster<br />
Zero Delay-Spread Cluster of Arrival<br />
Zero Delay-Spread Cluster of Departure<br />
Page 10 (167)
WINNER D5.4 v. 1.4<br />
PART I<br />
The deliverable D5.4 is divided into two major parts. This first part is the<br />
relatively short main part <strong>and</strong> c<strong>on</strong>tains the essence of the deliverable,<br />
specifically the <strong>channel</strong> model definiti<strong>on</strong>.<br />
Page 11 (167)
WINNER D5.4 v. 1.4<br />
1. Introducti<strong>on</strong><br />
WINNER project is aiming at a Bey<strong>on</strong>d-3G (B3G) radio <strong>system</strong> using a frequency b<strong>and</strong>width of 100<br />
MHz for <strong>on</strong>e radio c<strong>on</strong>necti<strong>on</strong> <strong>and</strong> a radio frequency lying most probably somewhere between 2 <strong>and</strong> 6<br />
GHz in spectrum. The research c<strong>on</strong>cerning the suitability of certain communicati<strong>on</strong> parameters, like<br />
modulati<strong>on</strong>, coding, symbol rate, MIMO antenna utilisati<strong>on</strong> etc., is performed through extensive<br />
simulati<strong>on</strong>s. The simulati<strong>on</strong> results depend str<strong>on</strong>gly <strong>on</strong> the radio <strong>channel</strong>. Hence, the radio <strong>channel</strong> is a<br />
crucial part of the simulati<strong>on</strong>. On <strong>on</strong>e h<strong>and</strong>, it is very important to use a very accurate <strong>and</strong> realistic<br />
<strong>channel</strong> model in the simulati<strong>on</strong> to enable reliable simulati<strong>on</strong> results. On the other h<strong>and</strong>, the complexity<br />
of the simulati<strong>on</strong> should be kept low. Therefore, the research challenge is to create a <strong>channel</strong> model which<br />
is realistic enough <strong>and</strong> simple.<br />
WINNER Work Package 5 (WP5) is focused <strong>on</strong> multi-dimensi<strong>on</strong>al radio <strong>channel</strong> modelling. Totally six<br />
partners are involved in WP5, namely Elektrobit (EBIT, in year 2004, <strong>and</strong> Elektrobit Testing EBITT in<br />
year 2005), Helsinki University of Technology (HUT), Nokia (NOK), Royal Institute of Technology in<br />
Stockholm (KTH), Swiss Federal Institute of Technology Zurich (ETHZ), <strong>and</strong> Technical University of<br />
Ilmenau (TUI). Up to now, the situati<strong>on</strong> is such that there are no widely accepted <strong>channel</strong> <strong>models</strong><br />
available which are suitable for WINNER <strong>system</strong> parameters. Therefore, WINNER WP5 has to create<br />
new <strong>channel</strong> <strong>models</strong> needed in the project. For the initial purposes, WP5 selected <strong>and</strong> recommended two<br />
existing <strong>channel</strong> <strong>models</strong>, which are called initial <strong>channel</strong> <strong>models</strong> [D5.1]. The <strong>models</strong> are 3GPP/3GPP2<br />
Spatial Channel Model (SCM) [3GPP SCM] for outdoor simulati<strong>on</strong>s <strong>and</strong> IEEE 802.11n MIMO model<br />
[802.11n] for indoor simulati<strong>on</strong>s. Because the SCM model was not suitable for WINNER simulati<strong>on</strong>s as<br />
such, WP5 performed some modificati<strong>on</strong>s <strong>and</strong> implemented the extended SCM model (SCME) [SCME].<br />
However, in spite of these modificati<strong>on</strong>s, the initial <strong>channel</strong> <strong>models</strong> were not good enough for the<br />
advanced simulati<strong>on</strong>s. C<strong>on</strong>sequently new WINNER <strong>models</strong> are needed.<br />
The WINNER <strong>channel</strong> <strong>models</strong> were implemented in two steps. In the first step, <strong>channel</strong> <strong>models</strong> for the<br />
most urgently needed propagati<strong>on</strong> scenarios with a limited number of parameters were created.<br />
Propagati<strong>on</strong> scenario means here the propagati<strong>on</strong> envir<strong>on</strong>ment <strong>and</strong> certain propagati<strong>on</strong> related parameters<br />
specified to meaningful values. The main difference between different propagati<strong>on</strong> scenarios exists due to<br />
the diverse envir<strong>on</strong>ments. Channel model parameters were defined for five propagati<strong>on</strong> scenarios<br />
(prioritised scenarios) according to [D7.2], namely indoor small office (A1), urban micro-cell (B1),<br />
stati<strong>on</strong>ary feeder (B5), urban macro-cell (C2), <strong>and</strong> rural macro-cell (D1). These <strong>models</strong> are described in<br />
the deliverable D5.3 [D5.3]. In the sec<strong>on</strong>d step the <strong>channel</strong> <strong>models</strong> were upgraded so that more<br />
parameters are included in the <strong>models</strong>. Two more scenarios – indoor (B3) <strong>and</strong> suburban (C1) – are also<br />
included based <strong>on</strong> the feedback from other work packages. The <strong>channel</strong> <strong>models</strong> created in the first step,<br />
<strong>and</strong> updated in the sec<strong>on</strong>d step, are described in this deliverable, D5.4.<br />
In this deliverable, we describe a generic <strong>channel</strong> model framework that is subsequently used as a basis<br />
for the <strong>channel</strong> <strong>models</strong> of all scenarios, except B5. Furthermore, we present clustered delay line (CDL)<br />
<strong>models</strong> for calibrati<strong>on</strong> <strong>and</strong> comparis<strong>on</strong> simulati<strong>on</strong>s. The generic modelling approach allows the creati<strong>on</strong><br />
of virtually unlimited double directi<strong>on</strong>al radio <strong>channel</strong> realizati<strong>on</strong>s. The generic <strong>channel</strong> model is a raybased<br />
multi-<strong>link</strong> model that is antenna independent, scalable <strong>and</strong> capable of modelling <strong>channel</strong>s for<br />
MIMO c<strong>on</strong>necti<strong>on</strong>s. The <strong>models</strong> are based <strong>on</strong> the existing literature <strong>and</strong> the parameters extracted from<br />
eleven measurement campaigns performed by the WP5. The selecti<strong>on</strong> of the model parameters is based<br />
both <strong>on</strong> the measurements <strong>and</strong> informati<strong>on</strong> found in the literature. The measurements were performed by<br />
five partners, namely EBIT/EBITT, HUT, KTH, NOK, <strong>and</strong> TUI. Different <strong>channel</strong> sounders, most of<br />
them capable of measurements at 2 <strong>and</strong> 5 GHz frequency ranges <strong>and</strong> 100 MHz b<strong>and</strong>width, were used.<br />
Measurement results were analyzed using beam-forming <strong>and</strong> super-resoluti<strong>on</strong> methods. The analyzed<br />
items, e.g. path loss, shadow fading characteristics, power delay profiles, delay spreads, angle-spreads,<br />
<strong>and</strong> cross-polarisati<strong>on</strong> ratio (XPR), were analyzed for the scenarios of interest.<br />
In WP5 <strong>on</strong>e activity has been the implementati<strong>on</strong> of the 3GPP/3GPP2 SCM <strong>channel</strong> model. The model<br />
was implemented in software by the WP5. Later, its extensi<strong>on</strong> to 5 GHz frequency range <strong>and</strong> 100 MHz<br />
b<strong>and</strong>width [SCME] was implemented. The extensi<strong>on</strong> work has been published in [BGS+05].<br />
We have compiled a set of requirements from various documents, specifically the WP2 Channel Model<br />
Requirements, the WP5 Deliverable D5.2 [D5.2], WP7 deliverable D7.2 [D7.2], <strong>and</strong> <str<strong>on</strong>g>report</str<strong>on</strong>g>ed<br />
shortcomings of the <strong>channel</strong> <strong>models</strong> selected for initial usage [D5.1]. The main requirements are proper<br />
characterisati<strong>on</strong> of spatial properties for MIMO support, large set of possible <strong>channel</strong>s as well as some<br />
limited r<strong>and</strong>omness <strong>channel</strong>s, c<strong>on</strong>sistency in time, frequency <strong>and</strong> space, e.g. inherent <strong>link</strong> between angle<br />
spectrum <strong>and</strong> Doppler spectrum, time-variability of bulk parameters, <strong>and</strong> extended polarisati<strong>on</strong> support.<br />
The document is organized in a way to provide best readability. Its overall c<strong>on</strong>tent is divided into 2 major<br />
parts. The first part is relatively short <strong>and</strong> c<strong>on</strong>tains the core informati<strong>on</strong> provided in this deliverable. Part I<br />
begins with an introducti<strong>on</strong>, background informati<strong>on</strong> c<strong>on</strong>cerning our approach, <strong>and</strong> the requirements <strong>on</strong><br />
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the <strong>models</strong> defined within the WINNER project. It is followed by related scenario definiti<strong>on</strong>s. The major<br />
<strong>and</strong> last chapter of part I c<strong>on</strong>tains the brief but comprehensive definiti<strong>on</strong> of the <strong>channel</strong> <strong>models</strong>. Part II<br />
provides more elaborate background informati<strong>on</strong> <strong>on</strong> model development. It c<strong>on</strong>tains detailed discussi<strong>on</strong><br />
<strong>on</strong> our modelling approach, the underlying data of our <strong>models</strong> (measurements <strong>and</strong> literature review), <strong>and</strong><br />
the interpretati<strong>on</strong> thereof. Two more chapters are dedicated to the <strong>channel</strong> model implementati<strong>on</strong>, <strong>and</strong> the<br />
test <strong>and</strong> verificati<strong>on</strong> of the model. The document ends with references <strong>and</strong> further, so far unused results of<br />
this project.<br />
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2. WINNER Scenarios<br />
These are the propagati<strong>on</strong> scenarios defined in WINNER. Scenarios marked in bold are prioritized<br />
scenarios that were modelled <strong>and</strong> implemented as the WINNER <strong>channel</strong> <strong>models</strong>.<br />
Table 2.1: Propagati<strong>on</strong> scenarios defined in WINNER.<br />
Scenario Definiti<strong>on</strong><br />
LOS/N<br />
LOS<br />
Mob. AP ht UE ht Distance<br />
range<br />
Note<br />
A1<br />
In building<br />
Indoor small<br />
office / residential<br />
LOS/<br />
NLOS<br />
0–5<br />
km/h<br />
2 m 1 m 3 - 100 m Deterministic room layout<br />
A2<br />
In building<br />
Indoor to outdoor NLOS 0–5<br />
km/h<br />
AP inside <strong>and</strong> coverage<br />
outside the building.<br />
B1<br />
Hotspot<br />
Typical urban<br />
micro-cell<br />
LOS/<br />
NLOS<br />
0–70<br />
km/h<br />
Below RT,<br />
e.g. 10 m<br />
1.5 m 20 - 400 m<br />
B2<br />
Hotspot<br />
B3<br />
Hotspot<br />
B4<br />
Hotspot<br />
Bad urban NLOS 0–70<br />
km/h<br />
Indoor LOS 0–5<br />
km/h<br />
Outdoor to indoor NLOS 0–5<br />
km/h<br />
Airport-type. Coverage in<br />
shopping hall with BTS<br />
outside.<br />
B5a<br />
Hotspot<br />
LOS stat. feeder,<br />
rooftop to rooftop<br />
LOS 0 km/h Above RT. Above<br />
RT.<br />
30m - 8 km<br />
B5b<br />
Hotspot<br />
LOS stat. feeder,<br />
street-<strong>level</strong> to<br />
street-<strong>level</strong><br />
LOS 0 km/h 2-5 m 2-5 m<br />
B5c<br />
B5d<br />
LOS stat. feeder,<br />
below-rooftop to<br />
street-<strong>level</strong><br />
NLOS stat. feeder,<br />
rooftop to street<strong>level</strong><br />
LOS 0 km/h As B1. As B1. As B1. As B1.<br />
NLOS 0km/h As C2. 1.5-10 m. As C2. As C2.<br />
C1<br />
Metropol<br />
Suburban<br />
LOS/<br />
NLOS<br />
0–70<br />
km/h<br />
35 - 3000 m<br />
C2<br />
Metropol<br />
Typical urban<br />
macro-cell<br />
LOS/<br />
NLOS<br />
0–70<br />
km/h<br />
Above RT,<br />
e.g. 32 m<br />
1.5 m 35 - 3000 m<br />
C3<br />
Metropol<br />
C4<br />
Metropol<br />
C5<br />
Metropol<br />
Bad urban NLOS 0–70<br />
km/h<br />
Outdoor to indoor NLOS 0–70<br />
km/h<br />
LOS feeder LOS 0 km/h<br />
D1<br />
Rural<br />
Rural macro-cell<br />
LOS/<br />
NLOS<br />
0–200<br />
km/h<br />
Above RT,<br />
e.g. 45 m<br />
1.5 m 35m - 10 km<br />
D2<br />
Rural<br />
LOS moving<br />
networks (feeder)<br />
LOS 0–300<br />
km/h<br />
2.1 Scenario definiti<strong>on</strong>s<br />
In the following subsecti<strong>on</strong>s, we present WP5 view to the envir<strong>on</strong>ments of the five prioritized scenarios.<br />
2.1.1 Scenario A1: Indoor small office<br />
Scenario A1 envir<strong>on</strong>ment is described in [D7.2]. This represents typical office envir<strong>on</strong>ment, where the<br />
area per floor is 5000 m 2 , number of floors is 3 <strong>and</strong> room dimensi<strong>on</strong>s are 10 m x 10 m x 3 m <strong>and</strong> the<br />
corridors have the dimensi<strong>on</strong>s 100 m x 5 m x 3 m. The A1 indoor office model is illustrated in Figure 2.1.<br />
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Figure 2.1: Layout of the A1 indoor scenario.<br />
The measured envir<strong>on</strong>ment resembles this definiti<strong>on</strong>, but is not identical [WP5AR]. It is assumed that<br />
propagati<strong>on</strong> parameters can be deduced from these measurements.<br />
2.1.2 Scenario B1: Urban micro-cell<br />
This scenario is defined for envir<strong>on</strong>ment where both fixed stati<strong>on</strong> <strong>and</strong> mobile stati<strong>on</strong> antenna heights are<br />
below surrounding buildings <strong>and</strong> both are outdoors. This scenario covers both LOS <strong>and</strong> NLOS<br />
propagati<strong>on</strong> c<strong>on</strong>diti<strong>on</strong>s. The envir<strong>on</strong>ment is defined for Manhattan like grid. The envir<strong>on</strong>ment streets can<br />
be classified as a main street, where the fixed stati<strong>on</strong> is located, perpendicular streets <strong>and</strong> parallel streets.<br />
The scenario is defined for street distance from 20 m to 400 m. In this envir<strong>on</strong>ment, the radio propagati<strong>on</strong><br />
<strong>and</strong> cell shape are c<strong>on</strong>fined within the area defined by the surrounding buildings.<br />
2.1.3 Scenario B3: Indoor hotspot<br />
The scenario B3 is described in [D7.2] <strong>and</strong> represents a typical indoor hot spot applicati<strong>on</strong> with a wide<br />
coverage area but n<strong>on</strong>-ubiquitous <strong>and</strong> low mobility (0-5 km/h). In this scenario traffic of high density can<br />
be expected. Typically applicati<strong>on</strong> scenarios can be found in c<strong>on</strong>ference halls, factory halls, entrance halls<br />
of train stati<strong>on</strong>s <strong>and</strong> airports, where the indoor envir<strong>on</strong>ment is characterised by large distances. The<br />
dimensi<strong>on</strong>s of such large halls can range from 20 m x 20 m x 5 m up to more then 100 m in width <strong>and</strong><br />
length as well as 20 m in height. Both LOS <strong>and</strong> NLOS propagati<strong>on</strong> situati<strong>on</strong>s can be found in this<br />
scenario.<br />
2.1.4 Scenario B5: Stati<strong>on</strong>ary feeder<br />
The definiti<strong>on</strong> of this scenario is less well understood by WP5 than are the others. WP5 found that that<br />
NLOS cases are also of interest for the feeder applicati<strong>on</strong>s. We therefore discuss <strong>models</strong> for NLOS cases<br />
as well. The following different feeder scenarios have been studied:<br />
• B5a Hotspot LOS stati<strong>on</strong>ary feeder: rooftop-to-rooftop<br />
• B5b Hotspot LOS stati<strong>on</strong>ary feeder: street-<strong>level</strong>-to-street-<strong>level</strong>.<br />
• B5c Hotspot LOS stati<strong>on</strong>ary feeder: blow-rooftop-to-street-<strong>level</strong><br />
• B5d Hotspot NLOS stati<strong>on</strong>ary feeder: above-rooftop-to-street-<strong>level</strong>.<br />
The scenarios of B5a <strong>and</strong> B5b are discussed below:<br />
2.1.4.1 Scenario B5a: LOS stati<strong>on</strong>ary feeder: rooftop-to-rooftop<br />
Our underst<strong>and</strong>ing of this case is illustrated in Figure 2.2. Wireless feeder master-stati<strong>on</strong>, probably <strong>on</strong> an<br />
elevated building, is c<strong>on</strong>nected to <strong>on</strong>e or several wireless feeder peripheral stati<strong>on</strong>s. A hot-spot wireless<br />
access point is then c<strong>on</strong>nected to the peripheral. As indicated in the picture, a cable is needed to c<strong>on</strong>nect<br />
the roof-top wireless feeder peripheral antenna. Alternatively a wireless soluti<strong>on</strong> may be possible also for<br />
these hops but then requiring additi<strong>on</strong>al antennas <strong>and</strong> transceivers.<br />
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Feeder-<strong>link</strong><br />
Masterstati<strong>on</strong><br />
Peripheral<br />
Cable<br />
Hot-spot<br />
Figure 2.2: Illustrati<strong>on</strong> of LOS stati<strong>on</strong>ary feeder: rooftop-to-rooftop.<br />
2.1.4.2 Scenario B5b: LOS stati<strong>on</strong>ary feeder: street-<strong>level</strong> to street-<strong>level</strong><br />
Our underst<strong>and</strong>ing of this case in indicated in Figure 2.3. Both ends of the <strong>link</strong> are located a few meters<br />
above ground <strong>and</strong> the model is aimed for 2-5 meter antenna heights. In many cases it may be possible to<br />
place the antennas high enough such that the first Fresnel z<strong>on</strong>e is clear <strong>and</strong> therefore free-space<br />
propagati<strong>on</strong> c<strong>on</strong>diti<strong>on</strong>s apply.<br />
Hotspot<br />
<strong>and</strong> feederperipheral.<br />
Feeder-<strong>link</strong><br />
Hotspot<br />
<strong>and</strong> feedermaster.<br />
Figure 2.3: Illustrati<strong>on</strong> of wireless LOS feeder-<strong>link</strong>: street-<strong>level</strong>.<br />
2.1.5 Scenario C1: Suburban macro-cell<br />
The scenario C1 is defined for a suburban outdoor envir<strong>on</strong>ment, where the coverage is ubiquitous. In<br />
suburban macrocells base stati<strong>on</strong>s are located well above the rooftops to allow wide area coverage.<br />
Buildings are typically low residential detached houses with <strong>on</strong>e or two floors, or blocks of flats with a<br />
few floors. Occasi<strong>on</strong>al open areas such as parks or playgrouds between the houses make the envir<strong>on</strong>ment<br />
rather open. Streets have r<strong>and</strong>om orientati<strong>on</strong>s, <strong>and</strong> no urban-like regular strict grid structure is observed.<br />
Vegetati<strong>on</strong> is modest.<br />
2.1.6 Scenario C2: Urban macro-cell<br />
In typical urban macrocell, mobile stati<strong>on</strong> is at street <strong>level</strong> <strong>and</strong> fixed base stati<strong>on</strong> clearly above<br />
surrounding building heights. As for propagati<strong>on</strong> c<strong>on</strong>diti<strong>on</strong>s, n<strong>on</strong>- or obstructed line-of-sight is a comm<strong>on</strong><br />
case, since street <strong>level</strong> is often reached by a single diffracti<strong>on</strong> over the rooftop. The building blocks can<br />
form either a regular Manhattan type of grid, or have more irregular locati<strong>on</strong>s. Typical building heights in<br />
urban envir<strong>on</strong>ments are over four storeys. Outdoor-to-indoor modelling is not part of typical urban<br />
macrocell scenario, but is a different scenario (Table 2.1, C4).<br />
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2.1.7 Scenario D1: Rural macro-cell<br />
Scenario D1 is defined <strong>on</strong>ly through its size (100 km 2 ) <strong>and</strong> hexag<strong>on</strong>al cell lay-out in [D7.2].<br />
The rural envir<strong>on</strong>ment we measured is flat, c<strong>on</strong>sisting of mainly sparsely located houses al<strong>on</strong>g roads that<br />
lead trough fields <strong>and</strong> some small forests <strong>and</strong> a small village. This should be c<strong>on</strong>sidered when interpreting<br />
results based <strong>on</strong> our model.<br />
3. WINNER Channel Models<br />
This chapter describes WINNER MIMO <strong>channel</strong> <strong>models</strong> of seven propagati<strong>on</strong> scenarios for <strong>link</strong> <strong>level</strong> <strong>and</strong><br />
<strong>system</strong> <strong>level</strong> simulati<strong>on</strong>s. Link <strong>level</strong> is defined for a single communicati<strong>on</strong> <strong>link</strong>. System <strong>level</strong> is defined<br />
for multi communicati<strong>on</strong> <strong>link</strong>s <strong>and</strong> base stati<strong>on</strong>s. Five of these scenarios are the prioritized propagati<strong>on</strong><br />
scenarios defined in the WINNER project in [D7.2] for short range <strong>and</strong> wide area wireless<br />
communicati<strong>on</strong>s. The prioritized scenarios are: Scenario A1 for indoor small office envir<strong>on</strong>ments,<br />
Scenario B1 for microcell urban envir<strong>on</strong>ment, Scenario B5 for hotspot LOS stati<strong>on</strong>ary wireless feeder,<br />
Scenario C2 for Metropolitan ubiquitous coverage in macrocell urban envir<strong>on</strong>ment, Scenario D1 for<br />
macrocell rural envir<strong>on</strong>ment. The two additi<strong>on</strong>al scenarios are part of the WINNER <strong>channel</strong> model:<br />
Scenario B3 for indoor propagati<strong>on</strong> <strong>and</strong> Scenario C1 for macrocell suburban envir<strong>on</strong>ment.<br />
In this chapter, we provide descripti<strong>on</strong> of the generic <strong>channel</strong> model, which is based <strong>on</strong> the principles of<br />
the SCM [3GPP SCM], for scenario A1, B1, B3, C1, C2, <strong>and</strong> D1. We also present clustered delay line<br />
(CDL) <strong>models</strong> for the menti<strong>on</strong>ed six scenarios of interest to generic model, <strong>and</strong> stati<strong>on</strong>ary feeder <strong>models</strong><br />
for scenario B5. The generic <strong>channel</strong> model is a geometric-based stochastic <strong>channel</strong> model. The following<br />
subsecti<strong>on</strong>s describe the WINNER phase-I MIMO <strong>channel</strong> <strong>models</strong> at 5 GHz.<br />
3.1 Generic model<br />
We apply the framework of the generic <strong>channel</strong> modelling approach presented in Chapter 4 to WINNER<br />
scenarios A1, B1, B3, C1, C2, <strong>and</strong> D1. Scenario B5 is not c<strong>on</strong>sidered in the generic <strong>channel</strong> model since<br />
it is a stati<strong>on</strong>ary wireless feeder scenario, where transmitter <strong>and</strong> receiver ends are fixed. Scenario B5 is<br />
modelled separately as clustered (tapped) delay line model (CDL) in Secti<strong>on</strong> 3.2.4.<br />
The generic <strong>channel</strong> model generates a number of ZDSCs. Their delays <strong>and</strong> directi<strong>on</strong>al properties are<br />
extracted from statistical distributi<strong>on</strong>s that corresp<strong>on</strong>d to a specific scenario, which are obtained from<br />
measurement results or from literature. The number of ZDSCs varies from <strong>on</strong>e scenario to another.<br />
Indeed, the number of ZDSCs itself is a r<strong>and</strong>om variable. However, in order to reduce the complexity for<br />
simulati<strong>on</strong> purpose, it has been kept as a fixed parameter. The median of the number ZDSCs is selected.<br />
We fix the number of rays within each ZDSC to 10 rays that have same delays <strong>and</strong> powers <strong>and</strong> may differ<br />
in angles, either departure or arrival. The directi<strong>on</strong>al properties of each ZDSC may vary from <strong>on</strong>e<br />
scenario to another <strong>and</strong> from departure side to arrival side. The WINNER generic <strong>channel</strong> model is<br />
antenna independent. Hence, different antenna c<strong>on</strong>figurati<strong>on</strong>s can be supported. In later terminology, the<br />
down<strong>link</strong> is c<strong>on</strong>sidered, where the transmitter is the fixed stati<strong>on</strong> (BS) <strong>and</strong> the receiver is the mobile<br />
stati<strong>on</strong> (MS). However, the same <strong>models</strong> can also be used for up<strong>link</strong> simulati<strong>on</strong>s due to the reciprocity of<br />
the radio <strong>channel</strong>.<br />
3.1.1 Large-scale parameters<br />
The radio <strong>channel</strong> is in general not stati<strong>on</strong>ary. Nevertheless, over short periods of time <strong>and</strong> space, <strong>channel</strong><br />
parameters experience small variati<strong>on</strong>s, <strong>and</strong> the assumpti<strong>on</strong> of short-term stati<strong>on</strong>arity is often a very good<br />
approximati<strong>on</strong>. The parameters characterizing our <strong>channel</strong> model are called bulk parameters. The time<br />
durati<strong>on</strong>s, over which these bulk parameters are c<strong>on</strong>stant, are termed <strong>channel</strong> segments a.k.a. drops in the<br />
nomenclature of the SCM. Over time <strong>and</strong> space, bulk parameters change <strong>and</strong> we characterize this<br />
variability statistically.<br />
There are a large number of bulk parameters. Bulk parameters include detailed or low-<strong>level</strong> bulk<br />
parameters such as number of paths, path powers, path angles at both <strong>link</strong> ends, path elevati<strong>on</strong>s at both<br />
<strong>link</strong> ends, <strong>and</strong> path delays. To characterize the <strong>channel</strong> with fewer parameters, higher <strong>level</strong>, e.g. sec<strong>on</strong>dorder,<br />
statistics are extracted <strong>on</strong> a per-segment basis, which we denote large-scale or dispersi<strong>on</strong> metric<br />
parameters. Large-scale parameters characterize the distributi<strong>on</strong>s of <strong>and</strong> between previously menti<strong>on</strong>ed<br />
low-<strong>level</strong> bulk parameters. Because realisati<strong>on</strong>s of large-scale parameters are drawn <strong>on</strong>ly <strong>on</strong>ce per<br />
<strong>channel</strong> segment, they are bulk parameters themselves. The following large-scale parameters are<br />
c<strong>on</strong>sidered:<br />
• Shadowing. The log-normal shadowing (LNS) value is the comm<strong>on</strong> shadowing across (i.e., for<br />
all) clusters. The variability across clusters around the LNS is given by an additive (in logdomain)<br />
Gaussian distributi<strong>on</strong> with a fixed st<strong>and</strong>ard deviati<strong>on</strong> of 3 dB.<br />
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• Cross-polarizati<strong>on</strong> ratio (XPR). No distincti<strong>on</strong> is made between clusters <strong>and</strong> segments in the<br />
current model. Therefore, the resulting variability of XPR is equivalent if evaluated across<br />
clusters or across segments.<br />
• Total angle-spread <strong>and</strong> delay-spread. These parameters characterize the power dispersi<strong>on</strong> in<br />
angle <strong>and</strong> delay domain across clusters. Note that this is a high-<strong>level</strong> characterizati<strong>on</strong>. The more<br />
detailed properties of angle <strong>and</strong> delay dispersi<strong>on</strong> are each defined by a set of two variables. This<br />
is firstly, a mean angle <strong>and</strong> a delay offset for each single cluster, <strong>and</strong> sec<strong>on</strong>dly, an angle-spread<br />
<strong>and</strong> a delay-spread for each cluster. Here,<br />
• The angle-spread per cluster <strong>and</strong> delay-spread per cluster values are c<strong>on</strong>stants.<br />
• The mean angle per cluster <strong>and</strong> the delay offset per cluster distributi<strong>on</strong>s are functi<strong>on</strong>s of<br />
the total (per segment) angle-spread <strong>and</strong> the total (per segment) delay-spread.<br />
Large-scale parameters of the <strong>channel</strong> have clear influence <strong>on</strong> the <strong>channel</strong> characteristics. This can be<br />
noticed in delay domain characteristics through the RMS delay spread <strong>and</strong> in the angle domain through<br />
the RMS angle-spread in departure <strong>and</strong> in arrival. The RMS delay spread has influence <strong>on</strong> power delay<br />
spectrum <strong>and</strong> <strong>on</strong> the probability density functi<strong>on</strong> (pdf) of path delays through the parameter r τ .. The<br />
statistical distributi<strong>on</strong>s that generate spatial properties of the ZDSCs are functi<strong>on</strong>s of RMS angle-spread<br />
through azimuth angle propati<strong>on</strong>ality factor ( r ϕ ) <strong>and</strong> RMS azimuth angle-spread ( σ ϕ ) in the arrival side,<br />
<strong>and</strong> through departure angle proporti<strong>on</strong>ality factor ( r φ ) <strong>and</strong> RMS departure angle-spread ( σ φ ) in the<br />
departure side. The dispersi<strong>on</strong> parameters σ ϕ <strong>and</strong> σ φ are sometimes correlated with log-normal<br />
shadowing (LNS), which is important for interference calculati<strong>on</strong>s, h<strong>and</strong>over algorithms, etc. For each set<br />
of RMS delay spread <strong>and</strong> RMS angle-spread departure, RMS angle-spread departure arrival <strong>and</strong> LNS<br />
within each <strong>channel</strong> segment, correlati<strong>on</strong> between them has to be c<strong>on</strong>sidered. These large-scale<br />
parameters are often <str<strong>on</strong>g>report</str<strong>on</strong>g>ed in literature to have log-normal distributi<strong>on</strong>s.<br />
Our framework allows for any distributi<strong>on</strong> for the large-scale parameters <strong>and</strong> also introduces a modelling<br />
of the auto-correlati<strong>on</strong> over the service area. This is achieved by using scenario <strong>and</strong> parameter specific<br />
g ⋅ to transform the large-scale parameters into a domain where they can be treated as<br />
transformati<strong>on</strong>s ( )<br />
Gaussian. The mean, µ , cross-correlati<strong>on</strong> <strong>and</strong> auto-correlati<strong>on</strong> matrix R ( 0)<br />
are then defined in the<br />
transformed domain. The realizati<strong>on</strong>s of the large-scale parameters are then obtained as<br />
−1<br />
0.5<br />
−1<br />
0.5<br />
0.5<br />
5<br />
g R ? x, y + µ<br />
⋅<br />
R 0 is obtained as R ( 0) = EΛ<br />
0.<br />
( ( ) ), where g ( ) is the inverse transform, <strong>and</strong> ( )<br />
T<br />
from the eigen-decompositi<strong>on</strong> R( 0 ) = EΛE<br />
of R ( 0)<br />
. The auto-correlati<strong>on</strong> is achieved by generating m<br />
( m = 6 for A1, <strong>and</strong> m = 4 for all other scenarios) independent Gaussian r<strong>and</strong>om processes,<br />
? x, y = ξ1 x,<br />
y Kξ<br />
m<br />
x,<br />
y , each <strong>on</strong>e with mean zero <strong>and</strong> variance <strong>on</strong>e in the positi<strong>on</strong>s x, y where the<br />
( ) [ ( ) ( )] T<br />
mobiles are located. The auto-correlati<strong>on</strong> of the process c<br />
( x,<br />
y)<br />
2<br />
E{ ξ ( x y ) ξ ( x , y )} = exp( − r / λ ), where ( ) ( ) 2<br />
c<br />
1, 1 c 2 2<br />
∆<br />
c<br />
∆ r =<br />
no auto-correlati<strong>on</strong> mode (NACM) in which the parameters<br />
the r<strong>and</strong>om variable ?( x, y) [ ξ ( x,<br />
y) ( x y)<br />
] T<br />
1<br />
Kξ<br />
m<br />
,<br />
x<br />
1 − x0<br />
+ y1<br />
− y0<br />
m<br />
ξ is given by<br />
. However, we also define a<br />
λ , K ,λ are all set to zero, or equivalently,<br />
= , is r<strong>and</strong>omized independently for each locati<strong>on</strong>. The<br />
required parameters for generating the correlated large-scale parameters are thus the transformati<strong>on</strong><br />
~<br />
s ( x , y)<br />
= g( s( x,<br />
y)<br />
) (or actually its inverse), the mean µ <strong>and</strong> correlati<strong>on</strong> R ( 0)<br />
of the transformed largescale<br />
parameters, <strong>and</strong> the de-correlati<strong>on</strong> distance parameters λ , K 1<br />
,λm<br />
.<br />
This informati<strong>on</strong> is available in Table 3.1 to Table 3.5. Table 3.1 lists the distributi<strong>on</strong> functi<strong>on</strong> for each<br />
modelled parameter in each scenario. For normally distributed r<strong>and</strong>om variables the original <strong>and</strong><br />
transformed variable is identical, except for the delay-spread in scenario B3, where the transformati<strong>on</strong> is a<br />
9<br />
multiplicati<strong>on</strong> with a factor 10 (for numerical reas<strong>on</strong>s). For parameters of log-Gumbel <strong>and</strong> log-Logistic<br />
distributi<strong>on</strong>, the transformati<strong>on</strong> (<strong>and</strong> their inverse) are given by:<br />
~<br />
−<br />
s = g s = −Q<br />
1 F log s ,ν ,ς<br />
(3.1)<br />
<strong>and</strong><br />
( ) (<br />
Gumbel( 10<br />
( ) ))<br />
−1<br />
( ~ −1<br />
s = g s ) = exp log(10) F Q(<br />
~ s ),ν ,ς<br />
Gumbel<br />
−<br />
1<br />
( ( ))<br />
1 ( F Logistic 10<br />
)<br />
−1<br />
( log(10) F ( Q(<br />
~ s ),ν ,ς ))<br />
−<br />
( s) = −Q<br />
( log ( s)<br />
,ν ,ς )<br />
(3.2)<br />
~<br />
s = g<br />
, (3.3)<br />
−1<br />
s = g ( ~ s ) = exp<br />
Logistic<br />
−<br />
(3.4)<br />
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respectively, where F<br />
Gumbel( x,ν ,ς ) <strong>and</strong> Logistic ( x,ν,ς )<br />
distributi<strong>on</strong>s defined in Secti<strong>on</strong> 5.4.3, <strong>and</strong> Q −1<br />
( x)<br />
variables i.e.<br />
Q<br />
F are the CDF of the Gumbel <strong>and</strong> Logistic<br />
1<br />
x<br />
∫<br />
−∞<br />
( x) = exp⎜<br />
⎟dt<br />
2π<br />
is the inverse of the CDF for Gaussian r<strong>and</strong>om<br />
⎛ − t<br />
⎜<br />
⎝ 2<br />
2<br />
⎞<br />
⎟<br />
⎠<br />
. (3.5)<br />
In Table 3.3, the so-called positi<strong>on</strong> ν <strong>and</strong> scale ς parameters for the distributi<strong>on</strong>s are listed, except for<br />
Scenario A1 (with 6 instead of 4 parameters) which is listed in Table 3.5. This means that if the largescale<br />
parameter c is log-Gumbel or log-Logistic, the transformed distributi<strong>on</strong> will have zero mean, <strong>and</strong><br />
unit variance, i.e., µ<br />
c<br />
= 0 <strong>and</strong> R c, c ( 0) = 1.<br />
This can be understood by noting that the mean <strong>and</strong> variance<br />
are taken into account already in the transformati<strong>on</strong>. For log-normal distributi<strong>on</strong>s, we use the<br />
transformati<strong>on</strong><br />
~<br />
= g( s) = log ( s)<br />
(3.6)<br />
s<br />
10<br />
s<br />
g<br />
=<br />
−1<br />
(<br />
~<br />
~ s<br />
s ) = 10<br />
with the excepti<strong>on</strong> of shadow-fading (or sometimes called log-normal shadowing, LNS) where we use<br />
~<br />
= g( s) = 10log ( s)<br />
(3.8)<br />
s<br />
10<br />
s =<br />
−<br />
g<br />
1<br />
(<br />
~ 0.1 s<br />
s ) = 10<br />
~<br />
in order to get the transformed shadow-fading in dB scale. For a log-normal distributed parameter c , the<br />
mean<br />
µ <strong>and</strong> st<strong>and</strong>ard deviati<strong>on</strong> ( 0)<br />
c<br />
R are the mean ν <strong>and</strong> st<strong>and</strong>ard deviati<strong>on</strong> ς listed in Table 3.3.<br />
c,c<br />
For normally distributed bulk parameters no transformati<strong>on</strong> is required (i.e. the transformed <strong>and</strong><br />
untransformed value are identical) <strong>and</strong> thus the mean <strong>and</strong><br />
in Table 3.3.<br />
µ <strong>and</strong> st<strong>and</strong>ard deviati<strong>on</strong> ( 0)<br />
c<br />
c,c<br />
(3.7)<br />
(3.9)<br />
R are listed<br />
In Table 3.5, the cross-correlati<strong>on</strong> between the transformed parameters are listed for scenario A1, <strong>and</strong> in<br />
Table 3.2 for the other scenarios. In teRMS of R ( 0)<br />
, the cross-correlati<strong>on</strong> between parameters r <strong>and</strong> c is<br />
given by<br />
c r , c<br />
r,<br />
r<br />
r,<br />
c<br />
( 0)<br />
( 0) R ( 0)<br />
R<br />
= . (3.10)<br />
R<br />
Thus by combining the cross-correlati<strong>on</strong> <strong>and</strong> variance informati<strong>on</strong>, the matrix R ( 0)<br />
can be derived. In<br />
Table 3.3, a correlati<strong>on</strong> distance ∆ is listed for each large-scale parameter. The correlati<strong>on</strong> distance is<br />
based <strong>on</strong> fitting of a single exp<strong>on</strong>ential exp( − ∆r / ∆)<br />
to the auto-correlati<strong>on</strong> functi<strong>on</strong> of the transformed<br />
large-scale parameter. This value is based <strong>on</strong> measurements or literature or a combinati<strong>on</strong> thereof.<br />
However, since the true auto-correlati<strong>on</strong> actually follows the equati<strong>on</strong> (*) of Secti<strong>on</strong> 4.1.4.1.4, i.e.<br />
2<br />
E { ( x , y ) s( x y )} = R( ∆r)<br />
, ( ) ( ) 2<br />
R<br />
s<br />
1 1 2,<br />
2<br />
⎛<br />
⎜<br />
⎝<br />
⎛<br />
⎜<br />
⎝<br />
c,<br />
c<br />
∆ r = x<br />
(3.11)<br />
2 − x1<br />
+ y2<br />
− y1<br />
∆r<br />
⎞ ⎛ ∆r<br />
⎞⎞<br />
⎟<br />
K<br />
⎜ ⎟⎟<br />
(*). (3.12)<br />
λ1<br />
⎠ ⎝ λm<br />
⎠⎠<br />
0.5<br />
0.5,T<br />
( ∆r) = R ( 0) diag⎜exp⎜−<br />
⎟,<br />
,exp⎜−<br />
⎟⎟R<br />
( 0)<br />
. This means<br />
c,<br />
c , will be a mixture of the m exp<strong>on</strong>entials of (*). However,<br />
they are selected in a way that the results are roughly the same as the single exp<strong>on</strong>ential. The values of the<br />
“eigenvalue auto-correlati<strong>on</strong> distances” λ<br />
1,<br />
K ,λm<br />
are listed in Table 3.4. Note that there is no <strong>on</strong>e-to-<strong>on</strong>e<br />
mapping between any of the lambda parameters <strong>and</strong> any of the large-scale parameters. The correlati<strong>on</strong><br />
distance ∆ is included to allow a more easy interpretati<strong>on</strong> of the auto-regressive characteristics of the<br />
model.<br />
0.5<br />
T 0.5<br />
5<br />
where R ( 0)<br />
is obtained from the eigendecompositi<strong>on</strong> R( 0) = EΛE<br />
as R ( 0) = EΛ<br />
0.<br />
that each autocorrelati<strong>on</strong> functi<strong>on</strong>, R ( ∆r)<br />
The justificati<strong>on</strong> for the expressi<strong>on</strong> (*) is that it produces a model from which it is computati<strong>on</strong>ally simple<br />
to generate data, <strong>and</strong> which at the same time gives a fit to experimental auto-correlati<strong>on</strong> functi<strong>on</strong>s which<br />
is typically equally good as the single exp<strong>on</strong>ential modelling.<br />
The derivati<strong>on</strong> of some of parameters λ<br />
1,<br />
K ,λm<br />
for each scenario, <strong>and</strong> in some case also other<br />
parameters, are given in Secti<strong>on</strong> 5.4.13 below.<br />
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The values <strong>and</strong> distributi<strong>on</strong>s were obtained from measurements at 5 GHz <strong>and</strong> from literature.<br />
In simulati<strong>on</strong>s which include both LOS <strong>and</strong> NLOS mobiles, the large-scale parameters of the LOS <strong>and</strong><br />
NLOS mobiles are modelled as independent, <strong>and</strong> thus they should be generated separately.<br />
Delayspread<br />
AoD<br />
spread<br />
AoA<br />
spread<br />
σ<br />
τ<br />
σ<br />
φ<br />
σ<br />
ϕ<br />
Table 3.1: Distributi<strong>on</strong> functi<strong>on</strong>s of large-scale parameters.<br />
A1 B1 B3 C1 C2 D1<br />
LOS NLOS LOS NLOS LOS NLOS LOS NLOS NLOS LOS NLOS<br />
LN LN Gumb Gumb N N LN LN LN LN LN<br />
LN LN Logist Gumb N N LN LN LN LN LN<br />
LN LN Logist Gumb N N LN LN LN LN LN<br />
Shadowing LN LN LN LN LN LN LN LN LN LN LN<br />
AoD<br />
Elevati<strong>on</strong><br />
spread σ<br />
θ<br />
AoA<br />
Elevati<strong>on</strong><br />
spread σ<br />
ϕ<br />
LN<br />
LN<br />
LN<br />
LN<br />
N<br />
LN<br />
Gumb<br />
Logist<br />
Normal (Gaussian)<br />
Log-normal, i.e., log10(Gauss)<br />
Log-Gumbel<br />
Log-Logistic<br />
Scenarios<br />
Table 3.2: Cross-correlati<strong>on</strong> between large-scale parameters.<br />
B1 B3 C1 C2 D1<br />
LOS NLOS LOS NLOS LOS NLOS NLOS LOS NLOS<br />
σ<br />
φ vs σ<br />
τ 0.50 0.18 0.17 0.13 -0.29 0.3 0.4 -0.07 -0.35<br />
Cross-Correlati<strong>on</strong>s<br />
σ<br />
ϕ vs σ<br />
τ 0.76 0.42 -0.2 0.49 0.78 0.7 0.6 0.21 0.12<br />
σ<br />
ϕ vs LNS -0.45 -0.40 -0.17 0.11 -0.16 -0.3 -0.3 -0.11 0.13<br />
σ<br />
φ vs LNS -0.50 0.01 -0.32 -0.18 0.36 -0.4 -0.6 -0.07 0.60<br />
σ<br />
τ vs LNS -0.41 -0.65 0.17 0.34 -0.71 -0.4 -0.4 -0.71 -0.51<br />
σ<br />
φ vs σ<br />
ϕ 0.37 0.07 0.19 0.28 -0.35 0.3 0.4 -0.49 -0.15<br />
Note: Sign of LNS has been defined so that positive LNS means more received power at MS than predicted by PL<br />
model.<br />
Scenarios<br />
Table 3.3: Distributi<strong>on</strong>s parameters of large-scale parameters.<br />
B1 B3 C1 C2 D1<br />
LOS NLOS LOS NLOS LOS NLOS LOS LOS NLOS<br />
ν -7.38 -7.09 26 45 -8.8 -7.26 -6.63 -7.8 -7.6<br />
σ τ<br />
ζ 0.24 0.11 8.2 6.9 0.49 0.33 0.32 0.57 0.48<br />
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∆<br />
τ<br />
(m)<br />
6.0 5.0 4.5 1.82 64 40 40 64.2 36.3<br />
ν 0.40 1.24 26.4 38 1.14 0.53 0.93 1.22 0.96<br />
σ φ<br />
ζ 0.23 0.20 10.5 11.7 0.12 0.36 0.22 0.21 0.45<br />
∆<br />
φ 13.2 2.4 2.2 0.62 2.0 30 50 24.8 2.7<br />
σ ϕ<br />
LNS<br />
Notes:<br />
ν 1.4 1.6 13.1 9.5 1.61 1.67 1.72 1.52 1.52<br />
ζ 0.12 0.19 7.6 4.5 0.20 0.3 0.14 0.18 0.27<br />
∆<br />
ϕ<br />
(m)<br />
ζ<br />
(dB)<br />
∆<br />
LNS<br />
(m)<br />
1.6 3.2 0.83 0.61 18.2 30 50 3.5 15.1<br />
2.3 3.1 1.4 2.1<br />
4.0<br />
6.0<br />
8 8<br />
9.1 5.2 4.36 6.16 23.0 50 50 40 120<br />
1. Values for ∆ are merely provided for informati<strong>on</strong>. Values of λ (see table below) are used in<br />
coefficient generati<strong>on</strong>.<br />
2. Scenarios C1 LOS <strong>and</strong> D1 LOS c<strong>on</strong>tain two shadowing std. deviati<strong>on</strong>s; <strong>on</strong>e (top) for before <strong>and</strong><br />
<strong>on</strong>e (bottom) for after the path-loss breakpoint.<br />
Parameters:<br />
ν: Locati<strong>on</strong> parameter (i.e., mean in case of normal distributi<strong>on</strong>)<br />
ζ: Scale parameter (i.e., st<strong>and</strong>ard deviati<strong>on</strong> in case of normal distributi<strong>on</strong>)<br />
∆: Correlati<strong>on</strong> distance of normal variable<br />
3.5<br />
6.0<br />
8.0<br />
Table 3.4: Lambda parameters.<br />
A1 B1 B3 C1 C2 D1<br />
LOS NLOS LOS NLOS LOS NLOS LOS NLOS NLOS LOS NLOS<br />
λ<br />
1 (m) 2.0 3.5 2.0 5.0 4.5 7.0 40.0 44.0 50.0 3.0 15.0<br />
λ<br />
2 (m) 2.0 2.0 12.0 2.3 0.8 0.6 2.0 30.0 45.0 10.0 2.0<br />
λ<br />
3 (m) 2.0 3.0 3.0 3.0 4.0 1.8 35.0 30.0 40.0 60.0 15.0<br />
λ<br />
4 (m) 3.0 2.5 9.1 5.2 2.2 0.6 27.0 47.0 52.0 42.0 120.0<br />
λ<br />
5 (m) 3.5 5.0<br />
λ<br />
6 (m) 6.0 4.0<br />
Table 3.5: Distributi<strong>on</strong> parameters for A1 sub-scenarios.<br />
Scenario Correlati<strong>on</strong> coefficients St<strong>and</strong>ard<br />
deviati<strong>on</strong>s<br />
ς<br />
Means<br />
γ<br />
Decorrelati<strong>on</strong><br />
distance ∆<br />
(m)<br />
LOS 1,00 0,46 0,74 -0,68 0,49 0,63<br />
0,46 1,00 0,40 -0,05 0,77 0,38<br />
0.27<br />
0.31<br />
-7.40<br />
0.74<br />
7.00<br />
5.90<br />
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0,74 0,40 1,00 -0,44 0,42 0,83<br />
-0,68 -0,05 -0,44 1,00 -0,11 -0,28<br />
0,49 0,77 0,42 -0,11 1,00 0,44<br />
0,63 0,38 0,83 -0,28 0,44 1,00<br />
0.26<br />
3.10<br />
0.20<br />
0.22<br />
1.52<br />
0.00<br />
0.88<br />
0.94<br />
2.30<br />
6.00<br />
1.30<br />
3.50<br />
NLOS 1,00 -0,10 0,31 -0,50 -0,61 -0,05<br />
-0,10 1,00 -0,26 -0,01 0,20 -0,14<br />
0,31 -0,26 1,00 -0,41 -0,28 -0,19<br />
-0,50 -0,01 -0,41 1,00 0,25 0,10<br />
-0,61 0,20 -0,28 0,25 1,00 0,45<br />
-0,05 -0,14 -0,19 0,10 0,45 1,00<br />
0.19<br />
0.23<br />
0.14<br />
3.50<br />
0.21<br />
0.17<br />
-7.60<br />
1.30<br />
1.57<br />
0.00<br />
1.06<br />
1.10<br />
Order of parameters: delay-spread, AoD azimuth-spread, AoA azimuth-spread, shadowing, AoD<br />
elevati<strong>on</strong>-spread, AoA elevati<strong>on</strong>-spread<br />
4.20<br />
4.90<br />
2.50<br />
3.40<br />
3.20<br />
2.60<br />
3.1.2 Average power of ZDSC c<strong>on</strong>diti<strong>on</strong>ed <strong>on</strong> their delays<br />
The average power of every ZDSC is calculated in delay domain as explained in Chapter 4. Two<br />
functi<strong>on</strong>s are required to calculate the expected power of each ZDSC c<strong>on</strong>diti<strong>on</strong>ed <strong>on</strong> their delays. They<br />
are the power delay spectrum <strong>and</strong> the probability density functi<strong>on</strong> of ZDSC delays. It is shown in<br />
Chapter 4, that for the case when both P ( τ ) <strong>and</strong> f ( τ ) are exp<strong>on</strong>ential, the expected power of ZDSC<br />
depends <strong>on</strong> the value of the parameter r τ <strong>and</strong> the RMS delay spread of the <strong>channel</strong> segmentσ . In order<br />
to make the average power of ZDSC varying from delay to delay <strong>and</strong> from <strong>on</strong>e <strong>channel</strong> segment to<br />
another in a similar manner that is usually seen in measurement results, the shadowing r<strong>and</strong>omizati<strong>on</strong><br />
effect <strong>on</strong> each ZDSC is modelled. Thus, the expected power of ZDSC of each segment is obtained as:<br />
'<br />
P n<br />
−ζ<br />
2 ⎛<br />
{ }<br />
( rτ<br />
−1) ⎞<br />
10<br />
α τ ∝ exp⎜−τ′<br />
⎟ 10<br />
= E<br />
⎜ rτ<br />
σ ⎟ ⋅ , (3.13)<br />
⎝<br />
τ ⎠<br />
where ζ<br />
n is an i.i.d. Gaussian r<strong>and</strong>om variable with zero mean <strong>and</strong> st<strong>and</strong>ard deviati<strong>on</strong> ζ , <strong>and</strong> the delay<br />
τ ′ is the normalized delay to the delay of the first arrival ZDSC. The normalized delay of the first arrival<br />
ZDSC is zero. For the case when the ZDSC delays have uniform distributi<strong>on</strong>, the expected power of<br />
ZDSC of each segment is obtained as:<br />
'<br />
P n<br />
−ξn<br />
2<br />
10<br />
{ α τ } ∝ exp( −τ<br />
' σ τ<br />
) ⋅10<br />
= E<br />
(3.14)<br />
The ZDSC delay distributi<strong>on</strong>s <strong>and</strong> power delay spectrum of different scenarios are presented in Table 3.6.<br />
For calculati<strong>on</strong> of <strong>channel</strong>s with cross-polarisati<strong>on</strong>, the cross-polarisati<strong>on</strong> ratio (XPR) for vertical to<br />
horiz<strong>on</strong>tal (XPR V ) <strong>and</strong> for horiz<strong>on</strong>tal to vertical (XPR H ) are needed (for definiti<strong>on</strong> see 5.4.12). The values<br />
XPR V <strong>and</strong> XPR V of different scenarios are given in Table 3.6.<br />
n<br />
τ<br />
Scenarios<br />
ZDSC<br />
Delay<br />
distributi<strong>on</strong><br />
Table 3.6: Formulae for calculating the ZDSC power c<strong>on</strong>diti<strong>on</strong>ed <strong>on</strong> delay for the c<strong>on</strong>sidered<br />
scenarios <strong>and</strong> XPR V <strong>and</strong> XPR H .<br />
A1 B1 B3 C1 C2 D1<br />
LOS NLOS LOS NLOS LOS NLOS LOS NLOS NLOS LOS NLOS<br />
Exp Exp Exp Uniform<br />
(0,800ns)<br />
Exp<br />
(0,130ns)<br />
Exp<br />
(0,220ns)<br />
Exp Exp Exp Exp Exp<br />
rτ<br />
3.0 2.4 3.2 2.2 1.90 1.58 2.4 1.5 2.3 3.8 1.7<br />
rτ<br />
−1<br />
rτ<br />
−1<br />
rτ<br />
−1<br />
ϒ<br />
r r r<br />
τ<br />
τ<br />
ζ (dB) 3<br />
τ<br />
'<br />
−t 10<br />
P n ϒ στ<br />
− ξ<br />
n<br />
e 10 n<br />
1<br />
rτ<br />
−1<br />
rτ<br />
−1<br />
rτ<br />
−1<br />
rτ<br />
−1<br />
rτ<br />
−1<br />
rτ<br />
−1<br />
rτ<br />
−1<br />
r r r r r r r<br />
τ<br />
XPR V µ 11.4 9.7 8.6 8 0.5 0.1 7.9 3.3 7.6 6.9 7.9<br />
τ<br />
τ<br />
τ<br />
τ<br />
Page 22 (167)<br />
τ<br />
τ
WINNER D5.4 v. 1.4<br />
(dB)<br />
XPR H<br />
(dB)<br />
σ<br />
µ<br />
σ<br />
3.4 3.5 1.8 1.8 1.07 0.69 3.3 2.5 3.4 2.3 3.5<br />
10.4 10.0 9.5 6.9<br />
3.4 3.1 2.3 2.8<br />
Notes:<br />
Not<br />
avail.<br />
Not<br />
avail.<br />
Not<br />
avail.<br />
Not<br />
avail.<br />
3.7 5.7 2.3 7.2 7.5<br />
2.5 2.9 0.2 2.8 4.0<br />
1. For scenario B3, XPR H values are not available. In the <strong>channel</strong> model implementati<strong>on</strong>, these<br />
values have been substituted by XPR V .<br />
2. Distributi<strong>on</strong> of XPR is log-normal, i.e., XPR = 10 X/10 , where X is Gaussian with st<strong>and</strong>ard<br />
deviati<strong>on</strong> σ <strong>and</strong> mean µ.<br />
Average powers of the ZDSC are normalized so that the total power of all ZDSCs is equal to <strong>on</strong>e. Then,<br />
the normalized power of the nth ZDSC is<br />
P<br />
'<br />
n<br />
n<br />
= Q<br />
P<br />
∑<br />
n=<br />
1<br />
P<br />
'<br />
n<br />
(3.15)<br />
where Q is the number of ZDSCs. For the case when LOS model is used, the power of the direct<br />
comp<strong>on</strong>ent is c<strong>on</strong>sidered in the normalizati<strong>on</strong> such that the ratio of the direct power to the scattered power<br />
is the K-factor.<br />
'<br />
n<br />
n<br />
=<br />
Q<br />
P<br />
P<br />
( K + 1)∑<br />
n=<br />
1<br />
The K-factor for LOS scenarios can be calculated as given in Table 3.7.<br />
P<br />
'<br />
n<br />
, (3.16)<br />
k<br />
P D<br />
= . (3.17)<br />
k +1<br />
Table 3.7: K factor formulae for LOS scenarios.<br />
Scenarios A1 B1 B3 C1 D1<br />
K [dB] 8.7 + 0.051*d 3+ 0.0142d 6 - 0.26*d 17.1 – 0.021*d 3.7 + 0.019*d<br />
Distance d is in m.<br />
It should be noted that when LOS comp<strong>on</strong>ent exists, the ZDSC will have 10+1 rays.<br />
3.1.3 Directi<strong>on</strong>al distributi<strong>on</strong>s of ZDSCs<br />
There are two types of angle informati<strong>on</strong> for each ZDSC. These are the mean angle <strong>and</strong> the offset angles<br />
of each ray from the mean within each cluster. The zero mean azimuth departure (azimuth arrival) angle<br />
is the transmitter (receiver) broadside directi<strong>on</strong>. The generated mean angle of departures <strong>and</strong> angle of<br />
arrivals are relative to the direct path between transmitter <strong>and</strong> receiver with respect to the broadside of<br />
transmitter or receiver, respectively. The angle definiti<strong>on</strong>s <strong>and</strong> references that are used in the generic<br />
<strong>channel</strong> model are the same as those presented in [3GPP SCM]. The relati<strong>on</strong> between the mean azimuthdeparture<br />
<strong>and</strong> the mean azimuth-arrival probability density functi<strong>on</strong>s of each ZDSC <strong>and</strong> their delays is<br />
generated through correlated large-scale parameters used in the corresp<strong>on</strong>ding density functi<strong>on</strong>s. We<br />
fixed the power azimuth spectrum of each ZDSC at the departure <strong>and</strong> the arrival sides assuming it as<br />
Laplacian. The RMS angle-spread of each ZDSC is fixed to <strong>on</strong>e value, which may be different in<br />
departure from arrival <strong>and</strong> may vary from scenario to scenario. However, the power azimuth spectrum<br />
(PAS) <strong>and</strong> the angle-spread values (AS ) of each ZDSC can be changed in the model if needed. The<br />
distributi<strong>on</strong> parameters of the mean angle of departure (AoD) <strong>and</strong> the mean angle of arrival (AoA) the<br />
ZDSCs may vary from <strong>on</strong>e scenario to another. The ZDSC azimuth-departure PAS <strong>and</strong> azimuth-arrival<br />
PAS are defined by 10 rays having predefined offset angles for Laplacian PAS from the mean angles of<br />
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the ZDSC. The 10 rays are spaced in angle domain <strong>and</strong> have identical power. The power of each ray is<br />
P n /10, where P n is the average power of the nth ZDSC. The offset angle spacing depends <strong>on</strong> the value of<br />
AS of ZDSC_D for the departure side <strong>and</strong> value of AS of ZDSC_A for the arrival side. The AS<br />
<strong>and</strong><br />
φ<br />
AS are per ZDSC <strong>and</strong> are different from the<br />
ϕ<br />
φ<br />
ϕ<br />
σ <strong>and</strong>σ , respectively, which are the composite<br />
angle-spread involving all ZDSC. The rays of the ZDSC have r<strong>and</strong>om phases. The angle distributi<strong>on</strong>s <strong>and</strong><br />
power azimuth spectrum at the transmitter or receiver sides may vary from <strong>on</strong>e scenario to another. Table<br />
3.8 states the angle distributi<strong>on</strong>s of different scenarios that are defined in WINNER generic <strong>channel</strong><br />
model. The σ <strong>and</strong> σ influence in generati<strong>on</strong> of the ZDSC directi<strong>on</strong>al informati<strong>on</strong> through the<br />
φ<br />
ϕ<br />
parameters r<br />
φ <strong>and</strong>r ϕ , respectively. The locati<strong>on</strong> parameter for all distributi<strong>on</strong>s is zero <strong>and</strong> the scaling<br />
parameters are defined by r φ<br />
σ φ for departure angles <strong>and</strong> by r ϕ<br />
σ ϕ for arrival angles. The generati<strong>on</strong> of<br />
the angle offsets from the rays from cluster mean angle depends <strong>on</strong> AS<br />
φ for departure side <strong>and</strong> <strong>on</strong><br />
ASϕ<br />
arrival cluster. The offset angles are determined by multiplicati<strong>on</strong> of the angles spread per ZDSC<br />
with basis vector of offset angles (BO), e.g., AS<br />
φ *BO. Table 3.9 states the number of ZDSCs <strong>and</strong> the<br />
number of rays in each cluster as well as their AS<br />
φ <strong>and</strong> AS<br />
ϕ for the c<strong>on</strong>sidered scenarios. The angles<br />
of BO vector <strong>and</strong> the calculati<strong>on</strong> of the offset angles are presented in Table 3.10.<br />
ϕ<br />
φ<br />
Scenarios<br />
AoD<br />
distributi<strong>on</strong><br />
AoD<br />
scaling<br />
parameter<br />
AoA<br />
distributi<strong>on</strong><br />
AoA<br />
scaling<br />
parameter<br />
Table 3.8: Distributi<strong>on</strong>s of azimuth <strong>and</strong> departure angles.<br />
A1 B1 B3 C1 C2 D1<br />
LOS NLOS LOS NLOS LOS NLOS LOS NLOS NLOS LOS NLOS<br />
Wrapped Gaussian<br />
2.0σ φ 1.2σ φ 3.4σ φ 1.1σ φ 1.9σ φ 1.3σ φ 1.4σ ϕ 2.3σ φ 3.2σ φ 0.8σ ϕ 1.2σ<br />
φ<br />
Wrapped Gaussian<br />
1.7σ ϕ 2.1σ ϕ 3.6σ ϕ 3.6σ ϕ 1.5σ ϕ 1.6σ ϕ 1.8σ ϕ 1.8σ ϕ 3.2σ ϕ 2.2σ ϕ 1.3σ<br />
ϕ<br />
Scenarios<br />
Table 3.9: Number of ZDSCs <strong>and</strong> the number of rays in each cluster.<br />
A1 B1 B3 C1 C2 D1<br />
LOS NLOS LOS NLOS LOS NLOS LOS NLOS NLOS LOS NLOS<br />
Number of ZDSC 16 11 8 16 15 24 15 14 20 11 10<br />
Rays per ZDSC 10<br />
AS<br />
φ (deg) 5 5 3 10 4.7 5.5 5 2 2 1.5 1.5<br />
AS<br />
ϕ (deg) 5 5 18 22 5.4 12.5 5 10 15 3 3<br />
Table 3.10: Offset angles Rays within a ZDSC as a functi<strong>on</strong> of<br />
AS<br />
φ ,<br />
Ray number Basis vector offset angles (BO) Rays offset angles<br />
1,2 ± 0.0742<br />
3,4 ± 0.2532<br />
5,6 ± 0.4986<br />
7,8 ± 0.8913<br />
AS<br />
ϕ , AS ϕ , <strong>and</strong> AS θ .<br />
OA = AS X BO<br />
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9,10 ± 1.9718<br />
OA : Offset angles, BO: Basis vector of offset angles<br />
Rays angle offsets of different values of<br />
angle-spread values of<br />
AS<br />
φ or<br />
AS<br />
φ or<br />
AS<br />
ϕ in Table 3.9 can be obtained by multiplying the<br />
AS<br />
ϕ times each angle of the basis vector offset angles (BO). For<br />
example AS<br />
φ = 5; the offset angles of the rays within a ZDSC is OA = 5 x BO = [ ±0.2226 ±0.7596<br />
±1.4960 ±2.6740 ±5.9154].<br />
Elevati<strong>on</strong> angle informati<strong>on</strong> are important for indoor envir<strong>on</strong>ments, i.e., Scenario A1 <strong>and</strong> B3. The generic<br />
model includes elevati<strong>on</strong> plane for these scenarios. The elevati<strong>on</strong> plane model parameters of these two<br />
scenarios are given in Table 3.11.<br />
Table 3.11: Elevati<strong>on</strong> plane model parameters.<br />
Scenarios<br />
A1<br />
B3<br />
LOS NLOS LOS NLOS<br />
ν (S-dB) 0.88 1.06<br />
σ θ<br />
ζ (S-dB) 0.20 0.21<br />
∆<br />
τ (m) 1.3 3.2<br />
ν (S-dB) 0.94 1.10<br />
σ ϑ<br />
Elevati<strong>on</strong> AoD distributi<strong>on</strong><br />
ζ (S-dB) 0.22 0.17<br />
∆<br />
τ (m) 3.5 2.6<br />
Wrapped<br />
Gaussian<br />
AoD scaling parameter 1.9 1.4<br />
Elevati<strong>on</strong> AoA distributi<strong>on</strong><br />
Wrapped<br />
Gaussian<br />
AS θ (deg) 3 3<br />
AS ϕ (deg) 3 3<br />
Cross-Correlati<strong>on</strong>s<br />
σ<br />
θ vs σ<br />
τ 0.46 -0.61<br />
σ<br />
ϑ vs σ<br />
τ 0.74 -0.05<br />
σ<br />
θ vs LNS -0.05 0.25<br />
σ<br />
ϑ vs LNS -0.44 0.11<br />
σ<br />
ϑ vs σ<br />
θ 0.44 0.45<br />
3.1.4 Antenna gain<br />
In principle the <strong>channel</strong> model is antenna independent at both fixed stati<strong>on</strong> <strong>and</strong> mobile stati<strong>on</strong>. Any 2D<br />
antenna c<strong>on</strong>figurati<strong>on</strong> <strong>and</strong> pattern can be embedded in the model. If elevati<strong>on</strong> plane parameters are<br />
included, 3D antenna geometries can be embedded. For example, <strong>on</strong>e can use the 3GPP antenna pattern<br />
in WINNER model. The antenna pattern that has been used in [3GPP SCM] at the BS is 3-sector antenna<br />
used for each sector. It is specified by:<br />
A<br />
( γ )<br />
⎡ ⎛ γ ⎞<br />
=−min⎢12<br />
⎜ ⎟<br />
⎢ ⎝γ<br />
3dB<br />
⎣ ⎠<br />
2<br />
, Am<br />
⎤<br />
o<br />
o<br />
⎥, where 180 < φ
WINNER D5.4 v. 1.4<br />
where γ is defined as the angle between the directi<strong>on</strong> of interest <strong>and</strong> the boresight of the antenna. The<br />
γ<br />
3dB is the 3dB beamwidth in degrees, <strong>and</strong> A m is the maximum attenuati<strong>on</strong>. For a 3 sector scenario γ<br />
3dB<br />
is 70 degrees, A m = 20dB. However, other antenna patterns can also be used, if needed.<br />
3.1.5 Path-loss <strong>models</strong><br />
Path-loss <strong>models</strong> at 5 GHz for c<strong>on</strong>sidered scenarios have been developed based <strong>on</strong> measurement results<br />
or from literature. The developed path <strong>models</strong> are presented in Table 3.12 including the shadow fading<br />
values. The path-loss <strong>models</strong> have the form as in (3.21), where d is the distance between transmitter <strong>and</strong><br />
receiver, the fitting parameter A includes the path-loss exp<strong>on</strong>ent parameter <strong>and</strong> parameter B is the<br />
intercept.<br />
( d ) B<br />
PL = Alog +<br />
(3.19)<br />
Table 3.12: Path-loss <strong>models</strong>.<br />
Scenario path loss [dB] shadow<br />
fading<br />
st<strong>and</strong>ard<br />
dev.<br />
applicability<br />
range<br />
A1<br />
B1<br />
B3<br />
LOS 18.7 log 10 (d[m]) + 46.8 σ = 3.1 dB 3 m < d < 100 m<br />
NLOS 36.8 log 10 (d[m]) + 38.8 σ = 3.5 dB 3 m < d < 100 m<br />
LOS 22.7 log 10 (d[m])+41.0 σ = 2.3dB 10 m < d < 650 m<br />
NLOS 0.096 d 1 [m] + 65 +<br />
σ = 3.1dB 10 m < d 1 < 550 m<br />
(28 – 0.024d 1 [m]) log 10 (d 2 [m])<br />
w/2 < d 2 < 450 m *)<br />
LOS 13.4 log 10 (d[m]) + 36.9 s = 1.4 dB 5 m < d < 29 m<br />
NLOS 3.2 log 10 (d[m]) + 55.5 s = 2.1 dB 5 m < d < 29 m<br />
C1<br />
s = 4.0 dB<br />
LOS 23.8 log 10 (d) + 41.6<br />
23.8 log 10 (d BP ) ****) +)<br />
40.0 log 10 (d/d BP ) + 41.6 + s = 6.0 dB,<br />
30 m < d < d BP<br />
d BP < d < 5 km<br />
NLOS 40.2 log 10 (d[m]) + 27.7 **) σ = 8 dB 50 m < d < 5 km<br />
C2 NLOS 35.0 log 10 (d[m]) +38.4 ***) σ = 8 dB 50 m < d < 5 km<br />
D1<br />
σ = 3.5dB<br />
LOS 21.5 log 10 (d[m]) + 44.6<br />
21.5 log 10 (d BP ) ****) +)<br />
40.0 log 10 (d/d BP ) + 44.6 + σ = 6.0dB<br />
30 m < d < d BP<br />
d BP < d < 10 km<br />
NLOS 25.1 log 10 (d[m]) + 55.8 σ = 8.0dB 30 m < d < 10 km<br />
*)<br />
w is LOS street width, d 1 is distance al<strong>on</strong>g main street, d 2 is distance al<strong>on</strong>g perpendicular street.<br />
**) Validity bey<strong>on</strong>d 1 km not c<strong>on</strong>firmed by measurement data.<br />
***)<br />
Validity bey<strong>on</strong>d 2 kms not c<strong>on</strong>firmed by measurement data.<br />
****)<br />
d BP is the break-point distance: d BP = 4 h BS h MS / ?, where h BS is antenna height at BS, h MS is<br />
antenna height at MS, <strong>and</strong> ? is the wavelength. Validity bey<strong>on</strong>d d BP not c<strong>on</strong>firmed by measurement<br />
data.<br />
+)<br />
BS antenna heights in the measurements: C1 LOS: 11.7 m, D1: 19 – 25 m.<br />
3.1.6 Probability of line of sight<br />
System <strong>level</strong> simulati<strong>on</strong>s require the probability of line of sight for c<strong>on</strong>sidered scenarios A1, B1, B3, C1,<br />
C2, <strong>and</strong> D1. They are given as follows:<br />
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3.1.6.1 Scenario A1<br />
3.1.6.2 Scenario B1<br />
⎧<br />
1 d ≤ 2.5m<br />
⎪<br />
P = ⎨<br />
1 − 0.9( 1 − ( 1.24 − 0.61log )<br />
3<br />
) 13<br />
10( d) d > 2.5m<br />
⎪⎩<br />
⎧1 d ≤ 15m<br />
⎪<br />
P = ⎨ 3<br />
1 ( 1 ( 1.56 0.48log ( ))<br />
) 13<br />
⎪ − − −<br />
10<br />
d d > 15m<br />
⎩<br />
(3.20)<br />
(3.21)<br />
where<br />
d = d + d , <strong>and</strong> d 1 <strong>and</strong> d 2 are like in Table 2.9.<br />
2 2<br />
1 2<br />
3.1.6.3 Scenario B3<br />
For the big factory halls, airport <strong>and</strong> train stati<strong>on</strong>s:<br />
⎧1,<br />
d < 10m<br />
P LOS<br />
= ⎨<br />
(3.22)<br />
⎩exp(<br />
−(<br />
d −10) / 45)<br />
For big lecture hall or c<strong>on</strong>ference hall:<br />
⎪<br />
⎧ 1, d < 5m<br />
P LOS<br />
= ⎨ d − 5<br />
(3.23)<br />
1−<br />
,5 m < d < 40 m<br />
⎪⎩ 150<br />
3.1.6.4 Scenario C1<br />
d[m]<br />
P = exp( − )<br />
(3.24)<br />
500m<br />
3.1.6.5 Scenario C2<br />
For scenario C2, <strong>on</strong>ly NLOS is c<strong>on</strong>sidered. In this case P(LOS) = 0.<br />
3.1.6.6 Scenario D1<br />
3.1.7 Generati<strong>on</strong> of <strong>channel</strong> coefficients<br />
d[m]<br />
P = exp( − )<br />
(3.25)<br />
1000m<br />
The generati<strong>on</strong> of <strong>channel</strong> parameters is performed per <strong>channel</strong> segment. During each <strong>channel</strong> segment<br />
the AoAs <strong>and</strong> AoDs, <strong>and</strong> delays of each ZDSC are fixed while the <strong>channel</strong> goes through fast fading<br />
according to the virtual moti<strong>on</strong> of the MS, which has a velocity vector v. The assumed <strong>system</strong> has S<br />
antennas at the transmitter side <strong>and</strong> U antennas at the receiver side. The WINNER generic <strong>channel</strong> model<br />
is a geometric-based stochastic model. There are a large number of r<strong>and</strong>om variables that are incorporated<br />
in the modelling approach. Hence, many parameters must be fixed within the simulati<strong>on</strong> run to make the<br />
computati<strong>on</strong> time feasible. These parameters may differ from <strong>on</strong>e scenario to another. For instance the<br />
ZDSC angle-spread ( AS or AS ) is fixed for all departure <strong>and</strong> arrival ZDSCs but may have different<br />
φ<br />
ϕ<br />
angles. These parameters represent some of the characteristics of different scenarios.<br />
To obtain MIMO <strong>channel</strong> coefficients the following steps are followed:<br />
1) Select <strong>on</strong>e of the scenarios to be simulated: A1, B1, B3, C1, C2, or D1.<br />
2) Assign locati<strong>on</strong>s of transmitters (BS), receivers (MS), separating distance <strong>and</strong> their antenna<br />
orientati<strong>on</strong>s. The orientati<strong>on</strong> of MS antenna is drawn from iid uniform distributi<strong>on</strong> U(0 o ,360 o ).<br />
Assign velocity vector to each MS. Assign LOS situati<strong>on</strong> to each locati<strong>on</strong> according to the<br />
probability.<br />
3) Calculate the path loss associated with transmitter-receiver of every MS <strong>and</strong> every BS if needed.<br />
4) Generate the vector ?( x, y)<br />
in the points i<br />
yi<br />
−1<br />
0.5<br />
obtain the large-scale parameters as R ?( x, y)<br />
x , where MSs are located, see Secti<strong>on</strong> 6.1.3. Then<br />
( µ )<br />
g +<br />
, the parameters can be found in the<br />
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0.5<br />
Tables of Secti<strong>on</strong> Error! Reference source not found.. Note that ( 0)<br />
0.5<br />
5<br />
R ( 0) = EΛ<br />
0.<br />
T<br />
from the eigen-decompositi<strong>on</strong> R( 0 ) = EΛE<br />
of R ( 0)<br />
.<br />
R shall be obtained as<br />
5) Based <strong>on</strong> the generated large-scale parameters:<br />
a. Generate the delays, azimuth AoD <strong>and</strong> azimuth AoA of each ZDSC through r<strong>and</strong>om<br />
variable generators of the corresp<strong>on</strong>ding probability density functi<strong>on</strong>s of the selected<br />
scenario. Generate elevati<strong>on</strong> AoD <strong>and</strong> AoA for indoor Scenarios.<br />
b. Order the delays <strong>and</strong> normalize them to the smallest delay.<br />
h<br />
c. Calculate the average power of each ZDSC within the <strong>channel</strong> segment as described in<br />
Secti<strong>on</strong> 3.1.2. Assign the power of each ray within the ZDSC as P n<br />
/ M , where M is<br />
the number of rays within ZDSC of a specific scenario, which is fixed to 10 rays.<br />
d. AoA <strong>and</strong> AoD are sorted in ascending order of absolute values. Shortest delays are are<br />
assosiated to AoA <strong>and</strong> AoD with smallest absolute values. Respectively, l<strong>on</strong>gest delay<br />
is assosiated to AoA <strong>and</strong> AoD with largest absolute value.<br />
e. Assign angle offset of rays in departure <strong>and</strong> arrival from predefined set of offset angles<br />
of the selected scenario <strong>and</strong> assign r<strong>and</strong>om phases from U(0 o ,360 o ) to the 10 rays of the<br />
ZDSC. Table 3.10 shows how to obtain offset angles of rays as a functi<strong>on</strong> of anglespreads.<br />
f. R<strong>and</strong>omly couple departure rays to arrival rays.<br />
g. Determine the AoD <strong>and</strong> AoA for all rays within each ZDSC with respect to the<br />
broadside of transmitter <strong>and</strong> receiver, respectively.<br />
h. Determine the antenna gain at transmitter G ( AoD ) <strong>and</strong> receiver ( )<br />
where n is the nth ZDSC <strong>and</strong> m is the mth ray within the nth cluster.<br />
i. Apply path loss <strong>and</strong> shadowing to each ray within all ZDSCs.<br />
t<br />
n<br />
G AoA ,<br />
j. With the knowledge of MS velocity vector, fast fading of each ZDSC can be calculated<br />
for every <strong>channel</strong> segment, while the bulk parameters <strong>and</strong> MS locati<strong>on</strong>s remained fixed.<br />
k. For linear array c<strong>on</strong>figurati<strong>on</strong>, the <strong>channel</strong> coefficient h ( t)<br />
u , s,<br />
n<br />
due to the nth ZDSC<br />
for each antenna pair, element s from transmitter <strong>and</strong> element u from receiver is given<br />
by:<br />
j⎡<br />
⎣kd<br />
s sin( φm, n ) +Φm,<br />
n⎤<br />
⎦<br />
Gt( φmn<br />
.<br />
) e<br />
⋅<br />
M ⎜<br />
⎟<br />
jkdu<br />
sin( ϕ m,<br />
n)<br />
usn , ,<br />
() =<br />
nσ<br />
SF∑ ⎜<br />
r( ϕmn<br />
,<br />
) ⋅ ⎟ (3.26)<br />
m = 1 ⎜<br />
⎟<br />
jk v cos( ϕ −θ<br />
) t<br />
h t P G e<br />
⎛<br />
⎜<br />
⎝<br />
e<br />
Assuming that cluster n=1 is the <strong>on</strong>e with normalized delay τ 1 =0 (i.e. the cluster with the<br />
lowest delay of all), an opti<strong>on</strong>al LOS comp<strong>on</strong>ent may be taken into account as follows:<br />
LOS<br />
u , s,<br />
n<br />
( t)<br />
=<br />
using<br />
where<br />
1<br />
h<br />
K + 1<br />
m,<br />
n<br />
v<br />
⎞<br />
⎟<br />
⎠<br />
[ kd sin( θ ) +Φ ]<br />
j<br />
⎛<br />
s BS LOS<br />
G<br />
⎞<br />
⎜<br />
s(<br />
θBS<br />
) e<br />
⋅<br />
σ<br />
⎟<br />
SFK<br />
j(<br />
kdu<br />
sin( θ MS ) +Φ LOS )<br />
+ δ ( n −1)<br />
⋅ ⎜ Gu<br />
( θMS<br />
e<br />
⋅⎟<br />
(3.27)<br />
K + 1<br />
⎜<br />
jk v cos( θ MS −θ<br />
v ) t<br />
⎟<br />
⎝<br />
e<br />
⎠<br />
u, s,<br />
n<br />
)<br />
⎧1 for n=<br />
0<br />
δ ( n)<br />
= ⎨<br />
⎩ 0 else<br />
φ<br />
n,m is the azimuth angle of departure of mth ray within the nth ZDSC.<br />
ϕ<br />
n,m is the azimuth angle of arrival of mth ray within the nth ZDSC.<br />
M is the number of rays within ZDSC, which is 10.<br />
G ( φ<br />
,m<br />
) is the antenna gain of transmitter (BS) for the mth ray within the nth ZDSC.<br />
t<br />
n<br />
r<br />
n<br />
Page 28 (167)
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G ( ϕ<br />
,m<br />
) is the antenna gain of receiver (MS) for the mth ray within the nth ZDSC.<br />
r<br />
d s<br />
d u<br />
k<br />
n<br />
is the distance between antenna elements of the linear array at transmitter.<br />
is the distance between antenna elements of the linear array at receiver.<br />
is the wave number.<br />
Φ<br />
n,m is the phase of the mth ray within the nth ZDSC.<br />
v<br />
is the speed of the MS.<br />
θ<br />
v<br />
is the angle of the MS velocity vector.<br />
If cross-polarisati<strong>on</strong> is c<strong>on</strong>sidered, additi<strong>on</strong>al cross polarized rays per each ZDSC are generated with<br />
same angle <strong>and</strong> delay informati<strong>on</strong> as those of the co-polarized rays described earlier but different r<strong>and</strong>om<br />
xy ,<br />
phases ( Φ ) from uniform distributi<strong>on</strong> U(0°,360°). The complex field pattern at transmitter <strong>and</strong><br />
mn ,<br />
receiver for vertical polarisati<strong>on</strong><br />
receiver<br />
h<br />
F<br />
t<br />
,<br />
h<br />
r<br />
v<br />
F<br />
t<br />
,<br />
v<br />
F<br />
r<br />
respectively, <strong>and</strong> for horiz<strong>on</strong>tal polarisati<strong>on</strong> for transmitter <strong>and</strong><br />
vh<br />
F , respectively. The cross-polarized amplitude from vertical to horiz<strong>on</strong>tal ( κ<br />
mn , ) or<br />
hv<br />
horiz<strong>on</strong>tal to vertical κ<br />
mn , for each ray within each ZDSC are calculated from their corresp<strong>on</strong>ding XPR<br />
values given by lognormal distributi<strong>on</strong> of parameters given in Table 3.6 for different scenarios. The κ is<br />
selected for every ray with each cluster from indpenent lognormal distributi<strong>on</strong> with parameters given in<br />
Table 3.3. It is assumed that the XPR r<strong>and</strong>om variables of different rays are independent from angles <strong>and</strong><br />
delays. The <strong>channel</strong> coefficient with polarizati<strong>on</strong> can be calculated as given in [3GPP SCM] as<br />
h () t = Pσ<br />
usn , ,<br />
n SF<br />
M<br />
∑<br />
m = 1<br />
( φ )<br />
( φ )<br />
( ϕ )<br />
( ϕ )<br />
T<br />
⎛ v<br />
v<br />
⎡<br />
vv vh vh<br />
F ⎤<br />
t m, n ⎡exp( j<br />
mn ,<br />
) κ<br />
m, n<br />
exp( j<br />
,<br />
) ⎤ ⎡F<br />
⎤ ⎞<br />
⎜<br />
Φ<br />
Φ<br />
mn r m,<br />
n<br />
⎢ ⎥ ⎢<br />
⎥ ⎢ ⎥⋅⎟<br />
⎜ h<br />
hv hv hh<br />
h<br />
⎢Ft mn ,<br />
⎥ ⎢κ<br />
mn ,<br />
exp( jΦ m, n) exp( jΦ mn ,<br />
) ⎥ ⎢Fr mn ,<br />
⎥ ⎟<br />
⎜⎣ ⎦ ⎣<br />
⎦ ⎣ ⎦ ⎟<br />
⎜<br />
⎟<br />
⎜<br />
⎟<br />
jkds sin( φmn , ) jkdu sin( ϕm, n ) jk v cos( ϕm,<br />
n−θv<br />
) t<br />
⎜<br />
e e ⋅ e<br />
⎟<br />
⎜<br />
⎟<br />
⎜<br />
⎟<br />
⎝<br />
⎠<br />
(3.28)<br />
For LOS ray (not cluster), the off-diag<strong>on</strong>al elements of the polarizati<strong>on</strong> matrix are zero by definiti<strong>on</strong>, i.e.,<br />
the XPR (given by the two κ) is infinity for the LOS ray.<br />
3.2 Reduced variability “clustered delay line” model<br />
This <strong>channel</strong> model is somehow different from the c<strong>on</strong>venti<strong>on</strong>al tapped delay line <strong>models</strong> in a sense that<br />
fading within each tap is generated by a sum of sinusoids i.e., the rays within the cluster of that tap.<br />
However, it is based <strong>on</strong> similar principles of the ZDSC <strong>channel</strong> modelling approach. Clustered delay line<br />
(CDL) model is composed of a number of separate delayed clusters. Each cluster has a number of<br />
multipath comp<strong>on</strong>ents (rays) that have the same known delay values but differ in known angle of<br />
departure <strong>and</strong> known angle of arrival. The cluster’s angle-spread may be different from that of BS to that<br />
of the MS. The offset angles of the rays depend <strong>on</strong> the angles spread at BS or MS <strong>and</strong> are calculated as<br />
shown in Table 3.10. The offset angles represent the Laplacian PAS of each ZDSC. The average power,<br />
mean AoA, mean AoD of clusters, angle-spread at BS <strong>and</strong> angle-spread at MS of each cluster in the CDL<br />
are extracted or estimated from measurement results at 5 GHz <strong>and</strong> chip frequency (f c ) of 100 MHz for<br />
Scenarios A1, C2 <strong>and</strong> D1, <strong>and</strong> f c =60 MHz for scenario B1 or obtained from literature as in Scenario B5.<br />
In the CDL model each ZDSC is composed of 10 rays with fixed offset angles <strong>and</strong> identical power. In the<br />
case of ZDSC where a ray of dominant power exists, the ZDSC has 10+1 rays. This dominant ray has a<br />
zero angle offset. The departure <strong>and</strong> arrival rays are coupled r<strong>and</strong>omly. The CDL table of all scenarios of<br />
interest are give below, where the ZDSC power <strong>and</strong> the power of each ray are tabulated. The CDL <strong>models</strong><br />
offer well-defined radio <strong>channel</strong>s with fixed parameters to obtain comparable simulati<strong>on</strong> results with<br />
relatively n<strong>on</strong>-complicated <strong>channel</strong> <strong>models</strong>.<br />
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3.2.1 Scenario A1<br />
The number of different taps in the delay line has been selected according to the measurements data. The<br />
number selected is larger than the median value to represent an envir<strong>on</strong>ment that is more dem<strong>and</strong>ing than<br />
average <strong>and</strong> also to provide a reas<strong>on</strong>ably low frequency correlati<strong>on</strong>. The offset angles of each ray within<br />
every ZDSC are calculated as shown in Table 3.10. The CDL parameters of LOS <strong>and</strong> NLOS c<strong>on</strong>diti<strong>on</strong> are<br />
given in Table 3.13 <strong>and</strong> Table 3.14, respectively.<br />
3.2.1.1 LOS<br />
ZDSC<br />
#<br />
delay<br />
[ns]<br />
Table 3.13: Scenario A1: LOS Clustered delay line model, indoor envir<strong>on</strong>ment.<br />
Power<br />
[dB]<br />
AoD<br />
[º]<br />
AoA<br />
[º]<br />
K-<br />
factor<br />
[dB]<br />
MS speed = 1 m/s,<br />
directi<strong>on</strong> U(0 o ,360 o )<br />
1 0 0 0 0 12.7 -0.23 * -22.9 **<br />
2 5 -1.7 4.25 -61.8 -10.7<br />
3 10 -6.2 0.54 -42.8 -16.2<br />
4 15 -7.7 -4.55 -33.9 -17.7<br />
5 20 -9.3 0.78 -27.2 -19.3<br />
6 25 -12.1 -3.14 16.6 -22.1<br />
7 30 -12.7 3.74 41.2 -22.7<br />
8 35 -12.7 -2.83 -15.6 -22.7<br />
9 45 -14.7 2.01 2.54 -24.7<br />
10 55 -16.3 5.83 -89.1 -26.3<br />
11 65 -16.8 -10.9 40.9 -26.8<br />
-∞<br />
Number of rays /ZDSC = 10 +<br />
Ray Power [dB]<br />
ZDSC AS at MS [º] = 5<br />
ZDSC AS at BS [º] = 5<br />
Composite AS at MS [º] = 32.5<br />
Composite AS at BS [º] = 5.1<br />
12 75 -18.4 -13.4 124.0<br />
-28.4<br />
*<br />
**<br />
+<br />
Power of dominant ray,<br />
Power of each other ray<br />
Clusters with high K-factor will have 11 rays.<br />
3.2.1.2 NLOS<br />
ZDSC #<br />
Table 3.14: Scenario A1: NLOS Clustered delay line model, indoor envir<strong>on</strong>ment.<br />
delay<br />
[ns]<br />
Power<br />
[dB]<br />
AoD<br />
[º]<br />
AoA<br />
[º]<br />
K-<br />
factor<br />
[dB]<br />
MS speed = 1 m/s,<br />
directi<strong>on</strong> U(0 o ,360 o )<br />
1 0 0 0 0 0<br />
2 5 -0.9 17.1 19.4 -10.9<br />
3 10 -1.5 -2.09 33.2 -11.5<br />
4 15 -1.6 4.99 15.2 -11.6<br />
5 20 -2.0 -11.4 -20.7 -12.0<br />
6 25 -2.6 -22.9 71.2 -12.6<br />
7 30 -3.4 43.4 48.3 -13.4<br />
8 35 -4.5 -32.2 92.6 -14.5<br />
9 40 -5.5 -22.0 49.0 -15.5<br />
10 45 -5.5 52.4 43.4 -15.5<br />
11 50 -5.0 1.57 -66.1 -15.0<br />
-∞<br />
Number of rays /ZDSC = 10<br />
Ray Power [dB]<br />
ZDSC AS at MS [º] = 5<br />
ZDSC AS at BS [º] = 5<br />
Composite AS at MS [º] = 39.1<br />
Composite AS at BS [º] = 23.2<br />
12 55 -4.7 37.8 -30.8<br />
-14.7<br />
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13 65 -5.4 8.39 -59.0 -15.4<br />
14 75 -9.0 -27.5 14.1 -19.0<br />
15 85 -11.3 -43.9 -7.76 -21.3<br />
16 95 -12.5 13.7 -0.59 -22.5<br />
17 105 -13.6 63.8 -13.4 -23.6<br />
18 115 -15.1 2.31 3.29 -25.1<br />
19 125 -16.8 8.77 4.22 -26.8<br />
20 135 -18.7 31.1 15.9 -28.7<br />
3.2.2 Scenario B1<br />
The parameters of the CDL model have been extracted from measurements with chip frequency of 60<br />
MHz at frequency range of 5.3 GHz. The number of ZDSCs is selected to be close to the median <strong>and</strong><br />
provide low frequency correlati<strong>on</strong>. The CDL parameters of LOS <strong>and</strong> NLOS c<strong>on</strong>diti<strong>on</strong> are given in Table<br />
3.15 <strong>and</strong> Table 3.16, respectively.<br />
3.2.2.1 LOS<br />
Table 3.15: Scenario B1: LOS Clustered delay line model.<br />
ZDSC #<br />
delay<br />
[ns]<br />
Power<br />
[dB]<br />
AoD<br />
[º]<br />
AoA<br />
[º]<br />
K-<br />
factor<br />
[dB]<br />
MS speed = 50 km/h,<br />
directi<strong>on</strong> U(0 o ,360 o )<br />
1 0 0 0 0 16 -0.11 * -26.11 **<br />
2 10 -1.2 -22 -10 9 -1.72 -20.7<br />
3 25 -7.4 -12 20 3 -9.16 -22.16<br />
4 30 -7.4 -12 20 -17.4<br />
5 45 -8.4 -2 -123 -18.4<br />
6 65 -13.0 10 -31 -23<br />
7 85 -15.1 -4 161 -25.1<br />
8 105 -16.1 8 -7<br />
-26.1<br />
-∞<br />
Number of rays<br />
/ZDSC =10 +<br />
Ray Power [dB]<br />
ZDSC AS at MS [º] = =18<br />
ZDSC AS at BS [º] =3<br />
Composite AS at MS [º]<br />
=37.1<br />
Composite AS at BS [º] =<br />
5.6<br />
*<br />
**<br />
+<br />
Power of dominant ray,<br />
Power of each other ray<br />
Clusters with high K-factor will have 11 rays.<br />
3.2.2.2 NLOS<br />
ZDSC<br />
#<br />
delay<br />
[ns]<br />
Power<br />
[dB]<br />
Table 3.16: Scenario B1: NLOS Clustered delay line model.<br />
AoD<br />
[º]<br />
AoA<br />
[º]<br />
K-<br />
factor<br />
[dB]<br />
MS speed = 50 km/h,<br />
directi<strong>on</strong> U(0 o ,360 o )<br />
1 0 -1.25 4 0 9 -1.8 * -20.8 **<br />
2 10 0 40 25 6 -1 * -17 *<br />
3 40 -0.38 -10. 29 -10.38<br />
4 60 -0.10 48. -31 -10.10<br />
5 85 -0.73 -36. 37 -10.73<br />
6 110 -0.63 -40 21 -10.63<br />
7 135 -1.78 -26 13<br />
-∞<br />
Number of rays /ZDSC<br />
= 10 +<br />
Ray Power [dB]<br />
-11.78<br />
ZDSC AS at MS [º]<br />
=22<br />
ZDSC AS at BS [º]<br />
=10<br />
Composite AS at MS<br />
[º] =36.4<br />
Composite AS at BS<br />
[º] =12.4<br />
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8 165 -4.07 -28 117 -14.07<br />
9 190 -5.12 -12 21 -15.12<br />
10 220 -6.34 -14 1 -16.34<br />
11 245 -7.35 14 15 -17.35<br />
12 270 -8.86 8 9 -18.86<br />
13 300 -10.1 -24 19 -20.1<br />
14 325 -10.5 -14 1 -20.5<br />
15 350 -11.3 -22 -13 -21.3<br />
16 375 -12.6 2 11 -22.6<br />
17 405 -13.9 8 -1 -23.9<br />
18 430 -14.1 -2 43 -24.1<br />
19 460 -15.3 -10 33 -25.3<br />
20 485 -16.3 -54 -19 -26.3<br />
*<br />
**<br />
+<br />
Power of dominant ray,<br />
Power of each other ray<br />
Clusters with high K-factor will have 11 rays.<br />
3.2.3 Scenario B3<br />
3.2.3.1 LOS<br />
ZDSC<br />
#<br />
Delay<br />
[ns]<br />
Power<br />
[dB]<br />
Table 3.17: Scenario B3: LOS Clustered delay line model.<br />
AoD<br />
[º]<br />
AoA<br />
[º]<br />
K-<br />
factor<br />
[dB]<br />
MS speed = 1.5 km/h,<br />
directi<strong>on</strong> U(0 o ,360 o )<br />
1 0 0 0 -0.9 26 -0.01 * -36 **<br />
2 5 -0.9 -4.0 1.3 7 -1.69 -18.69<br />
3 10 -1.8 -2.4 3.3 2 -3.92 -15.92<br />
4 15 -2.7 -0.9 1.9 -12.7<br />
5 20 -3.6 -2.5 10.5 -13.6<br />
6 25 -4.5 -0.4 14.9 -14.5<br />
7 30 -5.4 -15.1 4.6 -15.4<br />
8 40 -7.2 -3.9 3.3 -17.2<br />
9 50 -9.0 -5.1 5.8 -19.0<br />
10 60 -10.8 -2.0 1.4 -20.8<br />
11 70 -12.6 -23.6 7.6 -22.6<br />
12 80 -14.4 -16.3 4.5 -24.4<br />
13 90 -16.2 -8.2 6.2 -26.2<br />
- ∞<br />
Number of rays/ZDSC = 10<br />
Ray Power [dB]<br />
ZDSC AS at MS [º] = 5.4<br />
ZDSC AS at BS [º] = 4.7<br />
Composite AS at MS [º] = 18.1<br />
Composite AS at BS [º] = 3.7<br />
14 100 -18.0 50.9 -12.9 -28.0<br />
15 110 -19.8 -4.9 0.5 -29.8<br />
16 120 -21.6 -41.2 20.9<br />
-31.6<br />
*<br />
**<br />
+<br />
Power of dominant ray,<br />
Power of each other ray<br />
Clusters with high K-factor will have 11 rays.<br />
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3.2.3.2 NLOS<br />
ZDSC<br />
#<br />
delay<br />
[ns]<br />
Table 3.18: Scenario B3: NLOS Clustered delay line model.<br />
Power<br />
[dB]<br />
AoD<br />
[º]<br />
AoA<br />
[º]<br />
MS speed = 1.5 km/h.<br />
directi<strong>on</strong> U(0 o ,360 o )<br />
1 0 0 -19,3 -1,3 -10<br />
2 5 -0.5 -14,3 0,8 -10.5<br />
3 10 -1.08 -12,5 1 -11.08<br />
4 15 -1.63 -2,9 -1,7 -11.63<br />
5 20 -2.17 -34,4 9,3 -12.17<br />
6 25 -2.76 -12,1 9,1 -12.76<br />
7 30 -3.26 -20,8 9,7 -13.26<br />
8 40 -4.35 -6,8 2,2 -14.35<br />
9 50 -5.43 -5,6 1,2 -15.43<br />
10 60 -6.52 1,0 0,9 -16.52<br />
11 70 -7.61 -19,1 6,6 -17.61<br />
12 80 -8.69 -24,9 4,8 -18.69<br />
13 90 -9.78 -14,3 2,2 -19.78<br />
14 100 -10.87 48,0 -16,6 -20.87<br />
15 110 -11.95 24,9 -3,3 -21.95<br />
16 120 -13.04 -23,3 19,7 -23.04<br />
17 140 -15.21 -37,2 36,7 -25.21<br />
18 160 -17.38 39,2 -3,9 -27.38<br />
19 180 -19.56 29,2 -0,9 -29.56<br />
20 200 -21.73 25,2 -5,1 -31.73<br />
21 210 -22.82 -3,5 -18,9 -32.82<br />
22 220 -23.91 -25,6 -8,3<br />
-33.91<br />
K-factor = -∞<br />
Number of rays/ZDSC = 10<br />
Ray Power [dB]<br />
ZDSC AS at MS [º] = 12.5<br />
ZDSC AS at BS [º] = 5.5<br />
Composite AS at MS [º] = 18.7<br />
Composite AS at BS [º] = 3<br />
3.2.4 Scenario B5<br />
For the stati<strong>on</strong>ary feeder scenarios <strong>on</strong>ly CDL <strong>models</strong> have been created. The CDL <strong>models</strong> are based <strong>on</strong><br />
the parameters in the tables below which are derived from literature. Note that the CDL <strong>models</strong> <strong>on</strong>ly<br />
approximate the selected parameters. The motivati<strong>on</strong>s for each parameter can be found in Secti<strong>on</strong> xxx.<br />
Basically any antenna pattern can be used with the <strong>models</strong> However, for the B5 scenario at distances<br />
larger than 300 meters the 3 dB beamwidth γ of <strong>on</strong>e of the <strong>link</strong> ends should be smaller than 10<br />
3dB<br />
degrees while the other is smaller than 53 degrees. An example antenna patterns that can be used is:<br />
A<br />
( γ )<br />
⎡ ⎛ γ ⎞<br />
=−min⎢12<br />
⎜ ⎟<br />
⎢ ⎝γ<br />
3dB<br />
⎣ ⎠<br />
2<br />
, Am<br />
⎤<br />
o<br />
o<br />
⎥, where 180 < φ
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Table 3.19: Parameters selected for scenario B5a LOS stati<strong>on</strong>ary feeder: rooftop to rooftop.<br />
Parameter<br />
Value<br />
Path-loss (dB) Loss = .5 + 20log10( fc<br />
/ 2.5GHz) + 23.5log10( d)<br />
+ δ<br />
slow<br />
Shadow-fading<br />
Power-delay profile<br />
Delay-spread<br />
K-factor<br />
XPR<br />
Doppler<br />
Angle-spread of n<strong>on</strong>-direct comp<strong>on</strong>ents.<br />
36 ,<br />
30m< d 110dB), is given in Table 3.22, Table 3.23, <strong>and</strong> Table 3.24, respectively.<br />
Table 3.21: Parameters selected for scenario B5b LOS stati<strong>on</strong>ary feeder: street-<strong>level</strong> to street-<strong>level</strong>.<br />
Parameter<br />
Value<br />
Path-loss (dB) ( h − h )( h − h )<br />
r<br />
b<br />
= 4<br />
Loss<br />
Loss<br />
b<br />
0<br />
λ<br />
b<br />
0<br />
( r) = −20log( λ /( 4πr)<br />
) + σ free + δ free,<br />
r ≤ rb<br />
( r) = σ free − 20log10( λ /( 4πrb<br />
)) + 40log( r / rb ) + δ bey<strong>on</strong>d,<br />
r > rb<br />
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Shadow-fading s free=3dB, r ≤ rb<br />
,<br />
Range definiti<strong>on</strong><br />
Power-delay profile<br />
Delay-spread<br />
σ bey<strong>on</strong>d =7dB, r > rb<br />
Range 1: Loss
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ZDSC<br />
#<br />
delay<br />
[ns]<br />
Power<br />
[dB]<br />
AoD<br />
[º]<br />
AoA<br />
[º] Freq. of<br />
<strong>on</strong>e<br />
scatterer<br />
mHz<br />
K-<br />
factor<br />
[dB]<br />
MS speed N/A<br />
1 0 -1.5 0.0 0.0 744 13.0 -1.8 * -24.7 **<br />
2 5 -10.2 -71.7 70.0 -5 -20.2<br />
3 30 -16.6 167.4 -27.5 -2872 -26.6<br />
4 45 -19.2 -143.2 106.4 434 -29.2<br />
5 75 -20.9 34.6 94.8 294 -30.9<br />
6 90 -20.6 -11.2 -94.0 118 -30.6<br />
7 105 -16.6 78.2 48.6 2576 -26.6<br />
8 140 -16.6 129.2 -96.6 400 -26.6<br />
9 210 -23.9 -113.2 41.7 71 -33.9<br />
10 210 -12.0 -13.5 -83.3 3069 -22.0<br />
11 250 -23.9 145.2 176.8 1153 -33.9<br />
12 270 -21.0 -172.0 93.7 -772 -31.0<br />
13 275 -17.7 93.7 -6.4 1298 -27.7<br />
14 475 -24.6 106.5 160.3 -343 -34.6<br />
15 595 -22.0 -67.0 -50.1 -7 -32.0<br />
-∞<br />
Number of rays/ZDSC = 10 +<br />
Ray Power [dB]<br />
ZDSC AS at MS [º] = 2<br />
ZDSC AS at BS [º] = 2<br />
Composite AS at MS [º] =42.8<br />
Composite AS at BS [º] = 50.2<br />
16 690 -29.2 -95.1 -149.6 -186 -39.2<br />
17 855 -32.9 -2.0 161.5 -2288 -42.9<br />
18 880 -32.9 66.7 68.7 26 -42.9<br />
19 935 -28.0 160.1 41.6 -1342 -38.0<br />
20 1245 -29.6 -21.8 142.2 -61<br />
-39.6<br />
*<br />
**<br />
+<br />
Power of dominant ray,<br />
Power of each other ray<br />
Clusters with high K-factor will have 11 rays.<br />
ZDSC<br />
#<br />
delay<br />
[ns]<br />
Table 3.24: Clustered delay-line model street-<strong>level</strong> to street-<strong>level</strong> range 3.<br />
Power<br />
[dB]<br />
AoD<br />
[º]<br />
AoA<br />
[º] Freq. of<br />
<strong>on</strong>e<br />
scatterer<br />
mHz<br />
K-<br />
factor<br />
[dB]<br />
MS speed N/A<br />
1 0 -2.6 0.0 0.0 744 10.0 -3.0 * -23.0 **<br />
2 10 -8.5 -71.7 70.0 -5 -18.5<br />
3 90 -14.8 167.4 -27.5 -2872 -24.8<br />
4 135 -17.5 -143.2 106.4 434 -27.5<br />
5 230 -19.2 34.6 94.8 295 -29.2<br />
6 275 -18.8 -11.2 -94.0 118 -28.8<br />
7 310 -14.9 78.2 48.6 2576 -24.9<br />
8 420 -14.9 129.2 -96.6 400 -24.9<br />
9 630 -22.1 -113.2 41.7 71 -32.1<br />
10 635 -10.3 -13.5 -83.3 3069 -20.3<br />
-∞<br />
Number of rays/ZDSC = 10 +<br />
Ray Power [dB]<br />
ZDSC AS at MS [º] = 2<br />
ZDSC AS at BS [º] = 2<br />
Composite AS at MS [º] =52.3<br />
Composite AS at BS [º] = 61.42<br />
11 745 -22.2 145.2 176.8 1153<br />
-32.2<br />
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12 815 -19.2 -172.0 93.7 -772 -29.2<br />
13 830 -16.0 93.7 -6.4 1298 -26.0<br />
14 1430 -22.9 106.5 160.3 -343 -32.9<br />
15 1790 -20.3 -67.0 -50.1 -7 -30.3<br />
16 2075 -27.4 -95.1 -149.6 -186 -37.4<br />
17 2570 -31.1 -2.0 161.5 -2287 -41.1<br />
18 2635 -31.2 66.7 68.7 26 -41.2<br />
19 2800 -26.3 160.1 41.6 -1342 -36.3<br />
20 3740 -27.8 -21.8 142.2 -61 -37.8<br />
*<br />
**<br />
+<br />
Power of dominant ray,<br />
Power of each other ray<br />
Clusters with high K-factor will have 11 rays.<br />
3.2.5 Scenario C1<br />
3.2.5.1 LOS<br />
ZDSC<br />
#<br />
Table 3.25: Scenario C1: LOS Clustered delay line model, suburban envir<strong>on</strong>ment.<br />
delay<br />
[ns]<br />
Power<br />
[dB]<br />
AoD<br />
[º]<br />
AoA<br />
[º]<br />
K-factor<br />
[dB]<br />
MS speed = 50 km/h,<br />
directi<strong>on</strong> U(0 o ,360 o )<br />
1 0 0 0 0 10.0 -0.41* -20.4**<br />
2 5 -3.4 -15.9 -67.9 -13.4<br />
3 10 -2.7 9.34 -84.0 -12.7<br />
4 15 -2.6 -29.4 -51.2 -12.6<br />
5 20 -4.8 -6.32 -91.2 -14.8<br />
6 25 -7.5 -20.4 -94.5 -17.5<br />
7 30 -9.4 1.24 -22.8 -19.4<br />
8 35 -9.7 10.3 -17.2 -19.7<br />
9 40 -8.8 11.3 87.4 -18.8<br />
10 45 -8.9 5.12 -73.0 -18.9<br />
-∞<br />
11 50 -9.4 14.1 -120 -19.4<br />
12 70 -13.1 -18.9 -71.8 -23.1<br />
13 90 -14.2 2.84 2.87 -24.2<br />
14 110 -17.4 16.2 -128 -27.4<br />
15 130 -17.3 -14.2 20.5 -27.3<br />
16 150 -18.1 6.30 -48.2 -28.1<br />
17 170 -17.0 -4.64 52.0 -27.0<br />
18 190 -16.1 4.30 -43.5 -26.1<br />
19 210 -19.4 -0.79 11.9<br />
-29.4<br />
*<br />
**<br />
+<br />
Power of dominant ray,<br />
Power of each other ray<br />
Clusters with high K-factor will have 11 rays.<br />
Number of rays /ZDSC = 10 +<br />
Ray Power [dB]<br />
ZDSC AS at MS [º] = 5<br />
ZDSC AS at BS [º] = 5<br />
Composite AS at MS [º] = 45.8<br />
Composite AS at BS [º] = 14.2<br />
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3.2.5.2 NLOS<br />
Clustered delay line model has an RMS delay spread of 62 ns, <strong>and</strong> composite angle-spread of 53 <strong>and</strong> 5<br />
degrees in MS <strong>and</strong> BS, respectively. No K-factor is introduced for NLOS.<br />
Table 3.26: Clustered delay-line model for Scenario C1 NLOS<br />
ZDSC #<br />
delay<br />
[ns]<br />
Power<br />
[dB]<br />
AoD<br />
[º]<br />
AoA<br />
[º]<br />
K-<br />
factor<br />
[dB]<br />
MS speed = 50 km/h,<br />
directi<strong>on</strong> U(0 o ,360 o )<br />
1 0 0 0 0 -10<br />
2 5 -0.6 4 35 -10.6<br />
3 15 -1.8 -2 60 -11.8<br />
4 25 -2.3 -6 -39 -12.3<br />
5 60 -7.8 1 -56 -17.8<br />
6 80 -14.0 -5 165 -24.0<br />
7 105 -12.9 -8 -69 -22.9<br />
8 120 -9.8 -10 -109 -29.8<br />
9 205 -19.5 12 75 -29.5<br />
10 240 -17.4 22 120 -27.4<br />
11 255 -15.1 -25 138 -25.1<br />
-∞<br />
Number of rays /ZDSC = 10<br />
Ray Power [dB]<br />
ZDSC AS at MS [º] = 10<br />
ZDSC AS at BS [º] = 2<br />
Composite AS at MS [º] = 53<br />
Composite AS at BS [º] = 5<br />
12 350 -18.3 10 -177 -28.3<br />
13 380 -13.9 4 150 -23.9<br />
14 410 -19.9 -1 179<br />
-29.9<br />
3.2.6 Scenario C2<br />
Clustered delay line model has an RMS delay spread of 310 ns, <strong>and</strong> composite angle-spread of 53 <strong>and</strong> 8<br />
degrees in MS <strong>and</strong> BS, respectively. No K-factor is introduced. The parameters are given in Table 3.27.<br />
Table 3.27: Scenario C2: NLOS Clustered delay line model.<br />
ZDSC #<br />
delay<br />
[ns]<br />
Power<br />
[dB]<br />
AoD<br />
[º]<br />
AoA<br />
[º]<br />
K-<br />
factor<br />
[dB]<br />
MS speed = 50 km/h,<br />
directi<strong>on</strong> U(0 o ,360 o )<br />
1 0 -0.5 0 0 -10.5<br />
2 5 0.0 4 4 -10.0<br />
3 135 -3.4 -3 7 -13.4<br />
4 160 -2.8 -4 10 -12.8<br />
5 215 -4.6 -7 21 -14.6<br />
6 260 -0.9 8 -45 -10.9<br />
7 385 -6.7 10 -75 -16.7<br />
8 400 -4.5 17 65 -14.5<br />
9 530 -9.0 -8 160 -19.0<br />
10 540 -7.8 -8 155 -17.8<br />
11 650 -7.4 -4 88 -17.4<br />
-∞<br />
Number of rays /ZDSC = 10<br />
Ray Power [dB]<br />
ZDSC AS at MS [º] = 15<br />
ZDSC AS at BS [º] = 2<br />
Composite AS at MS [º] = 53<br />
Composite AS at BS [º] = 8<br />
12 670 -8.4 -7 80 -18.4<br />
13 720 -11.0 -9 -90 -21.0<br />
14 750 -9.0 -9 -105<br />
-19.0<br />
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15 800 -5.1 12 8 -15.1<br />
16 945 -6.7 -17 45 -16.7<br />
17 1035 -12.1 19 50 -22.1<br />
18 1185 -13.2 12 -15 -23.2<br />
19 1390 -13.7 19 -25 -23.7<br />
20 1470 -19.8 21 100 -29.8<br />
3.2.7 Scenario D1<br />
3.2.7.1 LOS<br />
Table 3.28: Scenario D1: LOS Clustered delay line model, rural envir<strong>on</strong>ment.<br />
ZDSC #<br />
delay<br />
[ns]<br />
Power<br />
[dB]<br />
AoD<br />
[º]<br />
AoA<br />
[º]<br />
K-<br />
factor<br />
[dB]<br />
MS speed = 120 km/h,<br />
directi<strong>on</strong> U(0 o ,360 o )<br />
1 0 0 0.0 0.0 10.9 -0.34 * -21.2 **<br />
2 5 -3.4 45.7 -25.5 -13.4<br />
3 10 -11.4 -12.7 35.6 -21.4<br />
4 15 -16.4 20.7 54.0 -26.4<br />
5 25 -17.8 -9.6 25.0 -27.8<br />
6 35 -17.9 -24.8 136.9 -27.9<br />
7 45 -18.8 -9.8 -7.6 -28.8<br />
8 55 -19.3 -9.6 21.5 -29.3<br />
9 65 -19.5 -21.3 -96.5 -29.5<br />
10 75 -18.5 8.2 -26.5 -28.5<br />
11 85 -19.0 21.9 -92.7 -29.0<br />
-∞<br />
Number of rays /ZDSC = 10 +<br />
Ray Power [dB]<br />
ZDSC AS at MS [º] = 1.5<br />
ZDSC AS at BS [º] = 1.5<br />
Composite AS at MS [º] = 24<br />
Composite AS at BS [º] = 21.5<br />
12 95 -19.6 23.2 -5.0 -29.6<br />
13 160 -18.7 28.7 -64.5<br />
-28.7<br />
* Power of dominant ray,<br />
** Power of each other ray,<br />
+<br />
Clusters with high K-factor will have 11 rays.<br />
3.2.7.2 NLOS<br />
Table 3.29: Scenario D1: NLOS Clustered delay line model, rural envir<strong>on</strong>ment.<br />
ZDSC<br />
#<br />
delay<br />
[ns]<br />
Power<br />
[dB]<br />
AoD<br />
[º]<br />
AoA<br />
[º]<br />
K-<br />
factor<br />
[dB]<br />
MS speed = 120 km/h,<br />
directi<strong>on</strong> U(0 o ,360 o )<br />
1 0 0 0 0 -10.0<br />
2 5 -1.0 -14.3 -27.1 -11.0<br />
3 10 -5.8 20.1 27.6 -15.8<br />
4 15 -8.2 21.6 -14.3 -18.2<br />
5 20 -9.0 14.5 15.9 -19.0<br />
6 25 -9.5 -13.9 -28.5 -19.5<br />
7 30 -10.1 7.06 23.7<br />
-∞<br />
Number of rays /ZDSC<br />
= 10 +<br />
Ray Power [dB]<br />
-20.1<br />
ZDSC AS at MS [º] =<br />
3<br />
ZDSC AS at BS [º] =<br />
1.5<br />
Composite AS at MS<br />
[º] = 17.9<br />
Composite AS at BS<br />
[º] = 22.4<br />
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8 35 -10.6 -66.7 -50.4 -20.6<br />
9 40 -11.1 9.92 50.5 -21.1<br />
10 45 -11.6 -21.3 32.0 -21.6<br />
11 50 -12.0 -34.9 15.7 -22.0<br />
12 65 -13.1 -4.88 12.7 -23.1<br />
13 80 -13.8 19.1 -7.40 -23.8<br />
14 95 -15.3 11.6 -4.82 -25.3<br />
15 110 -16.4 9.8 0.16 -26.4<br />
16 125 -16.8 -13.3 31.6 -26.8<br />
17 140 -17.9 -14.2 3.62 -27.9<br />
18 155 -18.6 71.1 14.6 -28.6<br />
19 170 -18.6 -20.2 27.4 -28.6<br />
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PART II<br />
This sec<strong>on</strong>d part c<strong>on</strong>tains more detailed informati<strong>on</strong> about our modelling<br />
approach, our measurements <strong>and</strong> literature analysis, <strong>and</strong> the <strong>channel</strong> model<br />
implementati<strong>on</strong>.<br />
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4. Modelling Approaches<br />
In this secti<strong>on</strong>, we discuss the modelling <strong>and</strong> coefficient generati<strong>on</strong> approach of existing spatial <strong>channel</strong><br />
<strong>models</strong>. Then our selected approach is presented in detail.<br />
Apart from the generic, fully r<strong>and</strong>om <strong>channel</strong> model, we define a clustered delay-line model derived from<br />
our generic model by limiting the r<strong>and</strong>omness (fixing the value) of certain parameters. The reduced<br />
variability aids the comparability of results based <strong>on</strong> shorter simulati<strong>on</strong> times.<br />
4.1 Generic <strong>channel</strong> modelling approach<br />
The generic <strong>channel</strong> modelling approach has been followed earlier in COST259 <strong>and</strong> in 3GPP<br />
st<strong>and</strong>ardizati<strong>on</strong>. In COST259, the approach has been followed for directi<strong>on</strong>al antenna <strong>channel</strong> <strong>models</strong> for<br />
smart antennas wireless applicati<strong>on</strong>s. The COST259 was mainly for antenna array applicati<strong>on</strong> at <strong>on</strong>e end,<br />
usually the base stati<strong>on</strong> side. The 3GPP st<strong>and</strong>ardizati<strong>on</strong> <strong>channel</strong> model, known as the 3GPP/3GPP2<br />
spatial <strong>channel</strong> model (SCM), was developed for MIMO approaches in third generati<strong>on</strong> cellular <strong>system</strong>s.<br />
A generic <strong>channel</strong> modelling approach can be thought as a <strong>channel</strong> model framework that can be applied<br />
in different scenarios. Each scenario has scenario specific distributi<strong>on</strong>s <strong>and</strong> parameters. By changing the<br />
scenario specific distributi<strong>on</strong>s in angle <strong>and</strong> delay domains as well as the scenario specific parameters, we<br />
can have different <strong>channel</strong> <strong>models</strong> for different scenarios under the same framework of the <strong>channel</strong><br />
model.<br />
4.1.1 Distincti<strong>on</strong> between <strong>channel</strong> <strong>models</strong> for <strong>link</strong>-<strong>level</strong> <strong>and</strong> <strong>system</strong>-<strong>level</strong> simulati<strong>on</strong><br />
Workpackage 5, as defined in the Annex, is divided into a <strong>link</strong>-<strong>level</strong> <strong>and</strong> a <strong>system</strong>-<strong>level</strong> modelling effort<br />
with task 4 representing the former <strong>and</strong> task 5 the latter part. During the evoluti<strong>on</strong> of our work though, we<br />
found that we had to be very careful with such a divisi<strong>on</strong> because it turned out not to be inherently clear<br />
where to draw the line. To counter this problem, we c<strong>on</strong>sequently defined a set of properties for each of<br />
the two <strong>level</strong>s in our deliverable D5.2. It has turned out most practical to implement both the <strong>link</strong>-<strong>level</strong><br />
<strong>and</strong> <strong>system</strong>-<strong>level</strong> features in <strong>on</strong>e model. Here we underst<strong>and</strong> the <strong>system</strong>-<strong>level</strong> <strong>channel</strong> modelling as in the<br />
SCM model [3GPP SCM]. Then it is possible to emphasize either the <strong>system</strong>-<strong>level</strong> features or the <strong>link</strong><strong>level</strong><br />
features or both by selecting the parameters properly.<br />
Our c<strong>on</strong>clusi<strong>on</strong> is that it can be potentially dangerous to define a certain divisi<strong>on</strong> <strong>and</strong> separate the two<br />
<strong>channel</strong> <strong>models</strong>. C<strong>on</strong>sider for example a model where shadowing is c<strong>on</strong>sidered a higher <strong>level</strong> than delayor<br />
angle-spread <strong>and</strong> for this reas<strong>on</strong> treated independently. As a c<strong>on</strong>sequence, a likely c<strong>on</strong>clusi<strong>on</strong> drawn<br />
from low-<strong>level</strong> simulati<strong>on</strong>s is that angle- <strong>and</strong> delay-spread significantly improves capacity. However, if<br />
all three parameters were simulated corporately, including their cross-correlati<strong>on</strong>s, the soluti<strong>on</strong> might be<br />
completely opposite, specifically that the capacity loss from shadowing outweighs the gain from delay<strong>and</strong><br />
angle-spread.<br />
In summary, we favour <strong>channel</strong> <strong>models</strong> that c<strong>on</strong>tain both the <strong>link</strong>-<strong>level</strong> <strong>and</strong> the <strong>system</strong>-<strong>level</strong> features<br />
defined at the same time. Hence, it depends <strong>on</strong> the applicati<strong>on</strong>, which feature is switched <strong>on</strong> or off.<br />
4.1.2 Comparis<strong>on</strong> between deterministic <strong>and</strong> stochastic <strong>channel</strong> modeling<br />
Channel modeling can be broadly split into two areas that differ in the goal or applicati<strong>on</strong> <strong>and</strong> the type of<br />
underlying data.<br />
Deterministic <strong>channel</strong> modeling can be employed when detailed envir<strong>on</strong>ment data is available. Detailed<br />
envir<strong>on</strong>ment data means positi<strong>on</strong>, size <strong>and</strong> orientati<strong>on</strong> of man-made objects (houses, buildings, bridges,<br />
roads, etc.) as well as natural objects (foliage or dominant plants, rocks, ground properties, etc.). The<br />
basic idea is that if the propagati<strong>on</strong> envir<strong>on</strong>ment is known to a sufficient degree, wireless propagati<strong>on</strong> is a<br />
deterministic process that allows determining or predicting its characteristics at every point in space. It is<br />
also referred to as propagati<strong>on</strong> predicti<strong>on</strong> <strong>and</strong> is the type of modeling used for cell planning, i.e., the<br />
analysis of optimum locati<strong>on</strong>s for BS deployment <strong>and</strong> the predicti<strong>on</strong> of the resulting coverage, capacity,<br />
<strong>and</strong> data rates. In deterministic <strong>channel</strong> modeling, <strong>channel</strong> measurements are made in the same<br />
envir<strong>on</strong>ment for which detailed data is available <strong>and</strong> then used to optimize the match between predicti<strong>on</strong><br />
model <strong>and</strong> measurements.<br />
Stochastic <strong>channel</strong> modeling <strong>on</strong> the other h<strong>and</strong> is based <strong>on</strong> a stochastic view of the wireless <strong>channel</strong>.<br />
Measurements are made in a large variety of locati<strong>on</strong>s <strong>and</strong> envir<strong>on</strong>ments to obtain a data set with a good<br />
representati<strong>on</strong> of the underlying statistical properties. Influence parameters based <strong>on</strong> the envir<strong>on</strong>ment<br />
characteristics may be used to refine the statistical accuracy for similar envir<strong>on</strong>ments. As such,<br />
classificati<strong>on</strong> is an important tool to trade off accuracy versus universality of statements.<br />
What we aim for in WINNER is the predicti<strong>on</strong> of statistical behavior of the <strong>channel</strong>. Knowledge of<br />
statistical <strong>channel</strong> parameters allows making more general statements. Especially, they allow evaluating<br />
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the properties <strong>and</strong> usefulness of communicati<strong>on</strong> schemes in case of large-scale deployment. Hence, we<br />
follow the stochastic <strong>channel</strong> modeling approach in our analysis.<br />
4.1.3 Interference modeling<br />
Interference modelling is an applicati<strong>on</strong> subset of <strong>channel</strong> <strong>models</strong> that deserves additi<strong>on</strong>al c<strong>on</strong>siderati<strong>on</strong>.<br />
Basically, communicati<strong>on</strong> <strong>link</strong>s that c<strong>on</strong>tain interfering signals are to be treated just as any other <strong>link</strong>.<br />
However, in many of today’s communicati<strong>on</strong> <strong>system</strong>s these interfering signals are not treated <strong>and</strong><br />
processed in the same way as the desired signals <strong>and</strong> thus modelling the interfering <strong>link</strong>s with full<br />
accuracy is inefficient.<br />
A simplificati<strong>on</strong> of the <strong>channel</strong> modelling for the interference <strong>link</strong> is often possible but closely <strong>link</strong>ed<br />
with the communicati<strong>on</strong> architecture. This makes it difficult for a generalized treatment in the c<strong>on</strong>text of<br />
<strong>channel</strong> modelling. In the following we will thus c<strong>on</strong>strain ourselves to giving some possible ideas of how<br />
this can be realised. Note that these are all combined signal <strong>and</strong> <strong>channel</strong> <strong>models</strong>. The actual<br />
implementati<strong>on</strong> will have to be based <strong>on</strong> the computati<strong>on</strong>al gain from computati<strong>on</strong>al simplificati<strong>on</strong> versus<br />
the additi<strong>on</strong>al programming overhead.<br />
AWGN interference<br />
The simplest form of interference is modelled by additive white Gaussian noise. This is sufficient for<br />
basic C/I (carrier to interference ratio) evaluati<strong>on</strong>s when coupled with a path loss <strong>and</strong> shadowing model. It<br />
might be extended with e.g. <strong>on</strong>-off keying (to simulate the n<strong>on</strong>-stati<strong>on</strong>ary behaviour of actual transmit<br />
signals) or other techniques that are simple to implement.<br />
Filtered noise<br />
The possible wideb<strong>and</strong> behaviour of an interfering signal is not reflected in the AWGN model above. An<br />
implementati<strong>on</strong> using a complex SCM or WIM <strong>channel</strong>, however, might be unnecessarily complex as<br />
well because the high number of degrees of freedom does not become visible in the noise-like signal<br />
anyway. Thus we propose something al<strong>on</strong>g the lines of a simple, sample-spaced FIR filter with Rayleighfading<br />
coefficients.<br />
Prerecorded interference<br />
A large part of the time-c<strong>on</strong>suming process of generating the interfering signal is the modulati<strong>on</strong> <strong>and</strong><br />
filtering of the signal, which has to be d<strong>on</strong>e at chip frequency. Even if the interfering signal is detected<br />
<strong>and</strong> removed in the communicati<strong>on</strong> receiver (e.g., multi-user detecti<strong>on</strong> techniques) <strong>and</strong> thus rendering a<br />
PN generator too simple, a method of precomputing <strong>and</strong> replaying the signal might be viable. The<br />
repeating c<strong>on</strong>tent of the signal using this technique is typically not an issue as the c<strong>on</strong>tent of the interferer<br />
is discarded anyway.<br />
4.1.4 Framework<br />
MIMO <strong>channel</strong> characterizati<strong>on</strong>, which takes into account directi<strong>on</strong>al characteristics at the transmitter <strong>and</strong><br />
receiver sides, is widely known as double directi<strong>on</strong>al <strong>channel</strong> modelling. We separate the effective radio<br />
<strong>channel</strong> in effects from wave propagati<strong>on</strong> <strong>on</strong> <strong>on</strong>e h<strong>and</strong> <strong>and</strong> antenna resp<strong>on</strong>se <strong>on</strong> the other h<strong>and</strong> to develop<br />
antenna independent MIMO <strong>channel</strong> model. By using the far-field, narrowb<strong>and</strong>, discrete wave, <strong>and</strong><br />
geometric diffracti<strong>on</strong> assumpti<strong>on</strong>, the effect of the antennas can be reduced to the effect of field pattern<br />
<strong>and</strong> to a phase shift based <strong>on</strong> the angle of the impinging wave, its wavelength, <strong>and</strong> the geometry of the<br />
antennas. This means that any antenna c<strong>on</strong>figurati<strong>on</strong>, orientati<strong>on</strong>, <strong>and</strong> pattern of antenna elements at both<br />
ends can be inserted in the model. In multipath envir<strong>on</strong>ment, each ray can be described by its path delay<br />
(τ), azimuth departure angle (φ), elevati<strong>on</strong> departure angle (θ), azimuth arrival angle (ϕ ), elevati<strong>on</strong><br />
arrival angle (ϑ ) <strong>and</strong> complex amplitude (α ) of the wave <strong>and</strong> polarisati<strong>on</strong> informati<strong>on</strong> matrix. The<br />
framework of the generic <strong>channel</strong> model is for all scenarios where <strong>on</strong>e terminal is mobile while the other<br />
is fixed. It is based <strong>on</strong> principles of existing work presented in [3GPP SCM], [SV87], [Cor01], [GEYC],<br />
[PMF00], [Fle00], [AlPM02], <strong>and</strong> generalized to MIMO case with elevati<strong>on</strong> angles at both ends. The<br />
generic model of MIMO <strong>channel</strong> for n<strong>on</strong>-stati<strong>on</strong>ary envir<strong>on</strong>ment can be described by <strong>channel</strong> impulse<br />
resp<strong>on</strong>se with horiz<strong>on</strong>tal <strong>and</strong> vertical polarisati<strong>on</strong> between antenna element s at transmitter <strong>and</strong> antenna<br />
element u at receiver as:<br />
u,<br />
s<br />
L(<br />
t)<br />
Mn(<br />
t)<br />
( t;<br />
τ,<br />
φ,<br />
θ,<br />
ϕ,<br />
ϕ)<br />
=∑ ∑<br />
e<br />
v<br />
T,<br />
s<br />
( φn,<br />
m,<br />
θn<br />
, m)<br />
( φ , θ )<br />
vv<br />
n,<br />
m<br />
j k( φ ( t) , θ ( t )),<br />
xT<br />
, s j k( ϕ ( t) , ϑ ( t)<br />
),<br />
x<br />
T<br />
vv vh<br />
vh<br />
( jΦn,<br />
m<br />
) κn , m<br />
exp( jΦn , m<br />
)<br />
hv hh<br />
hh<br />
( jΦ<br />
) κ exp( jΦ<br />
)<br />
h<br />
hv<br />
h<br />
n= 1 m=<br />
1 ⎢ T,<br />
s n,<br />
m n,<br />
m ⎥ ⎢ n,<br />
m n,<br />
m n,<br />
m<br />
n,<br />
m ⎥⎢<br />
R,<br />
u n,<br />
m n,<br />
m<br />
n,<br />
m<br />
⎛<br />
⎜⎡F<br />
⎜⎢<br />
F<br />
⎝⎣<br />
n,<br />
m<br />
e<br />
n,<br />
m<br />
⎤ ⎡κ<br />
⎥ ⎢<br />
⎦ ⎣κ<br />
n,<br />
m<br />
R,<br />
u<br />
exp<br />
exp<br />
e<br />
j2πνn,<br />
mt<br />
δ<br />
( τ −τ<br />
) δ( φ −φ<br />
) δ( θ −θ<br />
) δ( ϕ −ϕ<br />
) δ( ϑ −ϑ<br />
)<br />
n<br />
n,<br />
m<br />
⎤⎡F<br />
⎥⎢<br />
⎦⎣F<br />
v<br />
R,<br />
u<br />
( ϕn , m,<br />
ϑn,<br />
m<br />
) ⎤<br />
⎥ •<br />
( ϕ , ϑ ) ⎥⎦<br />
n,<br />
m<br />
n,<br />
m<br />
n,<br />
m<br />
(4.1)<br />
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where t is the time, the full polarimetric (2x2) transfer matrix, κ ( t)<br />
, includes the losses <strong>and</strong><br />
depolarisati<strong>on</strong> of all physical processes (reflecti<strong>on</strong>, diffracti<strong>on</strong>, scattering, etc) of each multipath<br />
r<br />
r<br />
comp<strong>on</strong>ents, xT,<br />
s is the positi<strong>on</strong> of the antenna element s of transmit antenna array, x Ru , is the positi<strong>on</strong><br />
of the antenna element u of the receive antenna array u, <strong>and</strong> ν<br />
l is the Doppler comp<strong>on</strong>ent. Note that all<br />
parameters are in general time variant, which is not shown for simpler presentati<strong>on</strong>.<br />
4.1.4.1 Inter-segment modeling<br />
The radio <strong>channel</strong> is in general not stati<strong>on</strong>ary. Nevertheless, over short periods of time <strong>and</strong> space, <strong>channel</strong><br />
parameters vary very little, <strong>and</strong> the assumpti<strong>on</strong> of short-term stati<strong>on</strong>arity is often a very good<br />
approximati<strong>on</strong>. The parameters characterizing our <strong>channel</strong> model are called bulk parameters. The time<br />
durati<strong>on</strong>s, over which these bulk parameters are c<strong>on</strong>stant, are denoted <strong>channel</strong> segment a.k.a. drops in the<br />
nomenclature of the SCM. Over time <strong>and</strong> space, bulk parameters change <strong>and</strong> we characterize this<br />
variability statistically.<br />
For simulati<strong>on</strong> purposes, the first goal typically is to experience the range of variability of the <strong>channel</strong><br />
rather than the medium-term evoluti<strong>on</strong> behaviour. Thus, the initial focus is mainly <strong>on</strong> the joint<br />
distributi<strong>on</strong>s of bulk parameters. Between <strong>channel</strong> segments, i.e. realizati<strong>on</strong>s of these r<strong>and</strong>om variables,<br />
independence is assumed. The physical interpretati<strong>on</strong> is that <strong>channel</strong> segments are relatively short <strong>channel</strong><br />
observati<strong>on</strong> periods that are significantly separated from each other in time or space.<br />
Each term of the matrix <strong>channel</strong> is a sum of L multipath comp<strong>on</strong>ents that can be described by the time<br />
htτφθϕϑ , , , , , , given as:<br />
varying double-directi<strong>on</strong>al delay spread functi<strong>on</strong> (D 3 SF), ( )<br />
L<br />
ht (, τφθϕϑ , , , , ) = ∑ αn() t δφ ( −φn, θ −θn, ϕ −ϕn, ϑ−ϑn, τ −τn)<br />
. (4.2)<br />
n=<br />
1<br />
The instantaneous power double-directi<strong>on</strong>al-delay spectrum (PD 3 S) can be written as:<br />
L<br />
2<br />
PI(, t τφθϕϑ , , , , ) = ∑ αn() t δφ ( −φn, θ −θn, ϕ −ϕn, ϑ−ϑn, τ −τn)<br />
, (4.3)<br />
n=<br />
1<br />
where ⋅ is the absolute value of the argument. The per <strong>channel</strong> segment (local) average PD 3 S, which<br />
represents <strong>channel</strong> characteristics per <strong>channel</strong> segment, can be defined as:<br />
P<br />
( τφθϕϑ , , , , ) = E { P ( t, τφθϕϑ , , , , )}<br />
s t I<br />
L<br />
∑<br />
= p δφ ( −φ , θ −θ , ϕ −ϕ , ϑ−ϑ ) δτ ( −τ<br />
)<br />
n=<br />
1<br />
n n n n n n<br />
In (4.4), the variables {L, p n , φ n , θ n , ϕ n , ϑ n , τ n } are the bulk parameters introduced above. The L paths are<br />
characterized by the orientati<strong>on</strong> of the last-bounce scatterer as seen from transmitter <strong>and</strong> receiver, as well<br />
as the total delay. This approach stems from superresoluti<strong>on</strong> parameter estimati<strong>on</strong> techniques (e.g.,<br />
MUSIC, ESPRIT, SAGE) which decompose a measured <strong>channel</strong> resp<strong>on</strong>se based <strong>on</strong> the above model.<br />
The D 3 SF characterizes the dispersive behaviour of the mobile radio <strong>channel</strong> in delay domain <strong>and</strong><br />
directi<strong>on</strong> domain seen either at transmitter or receiver sides. The equati<strong>on</strong>s are valid regardless of which<br />
terminal is the transmitter, either BS or MS <strong>and</strong> which terminal is the receiver either MS or BS. All<br />
parameters in (4.4) are r<strong>and</strong>om variables, since scatterers’ locati<strong>on</strong>s change with movement of the MS.<br />
Hence, the D 3 SF is a r<strong>and</strong>om process, which is described by joint distributi<strong>on</strong> of its r<strong>and</strong>om variables. The<br />
statistics of multipath comp<strong>on</strong>ents amplitudes, delays, <strong>and</strong> azimuth <strong>and</strong> elevati<strong>on</strong> angles at both ends are<br />
generally not separable. Hence, they have to be described in joint probability density functi<strong>on</strong>s (pdf).<br />
However, multidimensi<strong>on</strong>al joint pdf is not tractable mathematically. Therefore, simplificati<strong>on</strong>s are<br />
needed for simulati<strong>on</strong> purposes. As a result, <strong>on</strong>ly partial dependencies of distributi<strong>on</strong>s of different<br />
parameters are usually assumed.<br />
One of the most comm<strong>on</strong> assumpti<strong>on</strong>s is uncorrelated scattering (US). We assume independence of all<br />
parameters for different paths, i.e., different n. Therefore, (4.4) is characterized by the joint distributi<strong>on</strong><br />
f( pn, φn, θn, ϕn, ϑn, τ<br />
n)<br />
, which is independent of n.<br />
The expectati<strong>on</strong> in (4.4) is over short periods of time, where <strong>channel</strong> parameters vary <strong>on</strong>ly slightly, <strong>and</strong><br />
the assumpti<strong>on</strong> of short-term stati<strong>on</strong>arity is valid. The over segments (global) average PD 3 S is obtained<br />
by taking the expectati<strong>on</strong> of the per <strong>channel</strong> segment (local) average PD 3 S over all bulk parameters<br />
(4.4)<br />
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{ }<br />
( τφθϕϑ , , , , ) ( τφθϕϑ , , , , )<br />
P = E P s<br />
(4.5)<br />
The average PD 3 S in (4.4) is the average per <strong>channel</strong> segment, which may differ from <strong>on</strong>e segment to<br />
another. In order to relate (4.4) to the average PD 3 S over segments (4.5), all bulk parameters except the<br />
r<strong>and</strong>om powers can be pulled out of the expectati<strong>on</strong> to arrive at:<br />
P ( τφθϕϑ , , , , )<br />
{ (, , , , )| ,..., , ,..., , ,..., , ,..., , ,..., }<br />
= ∫ E P τφθϕϑ φ φ θ θ ϕ ϕ ϑ ϑ τ τ<br />
s 1 L 1 L 1 L 1 L 1 L<br />
L<br />
∏<br />
i=<br />
1<br />
f( φ, θ , ϕ , ϑ, τ ) dφdθdϕ dϑdτ<br />
i i i i i i i i i i<br />
(4.6)<br />
L<br />
= ∑ E{ pi<br />
| τφθϕϑ , , , , } f( τφθϕϑ , , , , )<br />
i=<br />
1<br />
{ τφθϕϑ}<br />
= LE p| , , , , f(, τφθϕϑ , , , ).<br />
From (4.6), the over segments (global) average PD 3 S (i.e., ( , , , , )<br />
P τφθϕϑ ) is equivalent to the<br />
c<strong>on</strong>diti<strong>on</strong>al expected power of the multipath comp<strong>on</strong>ents multiplied by the joint double-directi<strong>on</strong>al-delay<br />
probability density functi<strong>on</strong>.<br />
The power spectrum in each dimensi<strong>on</strong> is obtained by integrati<strong>on</strong> over other dimensi<strong>on</strong>s. Thus, power<br />
delay spectrum P ( τ ) , power azimuth-departure-angle spectrum P ( φ)<br />
, power elevati<strong>on</strong>-departure-angle<br />
spectrum P ( θ ) , power azimuth-arrival-angle spectrum P ( ϕ)<br />
, power elevati<strong>on</strong>-arrival-angle spectrum<br />
P can be derived as:<br />
( ϑ)<br />
( ) ( , , , , )<br />
P τ P τφθϕϑ dφdθdϕdϑ<br />
= ∫∫∫∫<br />
(4.7)<br />
( ) ( , , , , )<br />
P φ P τφθϕϑ dτdθdϕdϑ<br />
= ∫∫∫∫<br />
(4.8)<br />
( ) ( , , , , )<br />
P θ P τφθϕϑ dφdτdϕdϑ<br />
= ∫∫∫∫<br />
(4.9)<br />
( ) ( , , , , )<br />
P ϕ P τφθϕϑ dφdθdτdϑ<br />
= ∫∫∫∫<br />
(4.10)<br />
( ) ( , , , , )<br />
P ϑ P τφθϕϑ dφdθdϕdτ<br />
= ∫∫∫∫<br />
. (4.11)<br />
The corresp<strong>on</strong>ding marginal probability density functi<strong>on</strong>s (pdf) of parameters of each domain can be<br />
derived from:<br />
( ) ( , , , , )<br />
f τ f τφθϕϑ dφdθdϕdϑ<br />
= ∫∫∫∫<br />
(4.12)<br />
( ) ( , , , , )<br />
f φ f τφθϕϑ dd τ θdϕdϑ<br />
= ∫∫∫∫<br />
(4.13)<br />
( ) ( , , , , )<br />
f θ f τφθϕϑ dφdτdϕdϑ<br />
= ∫∫∫∫<br />
(4.14)<br />
( ) ( , , , , )<br />
f ϕ f τφθϕϑ dφdθdτdϑ<br />
= ∫∫∫∫<br />
(4.15)<br />
( ) ( , , , , )<br />
f ϑ f τφθϕϑ dφdθdϕdτ<br />
where f ( τ ), f ( φ)<br />
, f ( θ ), f ( ϕ)<br />
, f ( ϑ)<br />
, <strong>and</strong> ( , , , , )<br />
= ∫∫∫∫<br />
, (4.16)<br />
f τφθϕϑ are the pdf of path delays, the pdf of<br />
azimuth departure angles, the pdf of the elevati<strong>on</strong> departure angles, the pdf of the azimuth arrival angles,<br />
the pdf of the elevati<strong>on</strong> arrival angles, <strong>and</strong> the joint double-directi<strong>on</strong>al-delay probability density functi<strong>on</strong><br />
of argument parameters, respectively. Similarly, the P ( τ ) , P ( φ)<br />
, P ( θ ) , P ( ϕ)<br />
, P ( ϕ)<br />
can be expressed<br />
as:<br />
( ) { }<br />
P τ = LE p| τ f( τ)<br />
(4.17)<br />
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( ) { }<br />
P φ = LE p| φ f( φ)<br />
(4.18)<br />
( ) { }<br />
P θ = LE p| θ f( θ)<br />
(4.19)<br />
( ) { }<br />
P ϕ = LE p| ϕ f( ϕ)<br />
(4.20)<br />
( ) { }<br />
where E{ p|<br />
τ }, E{ p|<br />
φ } , E{ p|<br />
θ }, E{ p|<br />
ϕ } , <strong>and</strong> { | }<br />
P ϑ = LE p| ϑ f( ϑ)<br />
, (4.21)<br />
E p ϑ are the expected power of the<br />
multipath comp<strong>on</strong>ents c<strong>on</strong>diti<strong>on</strong>ed <strong>on</strong> their delays, azimuth departure angle, elevati<strong>on</strong> departure angle,<br />
azimuth arrival angle, elevati<strong>on</strong> arrival angle, respectively.<br />
4.1.4.1.1 Expected power c<strong>on</strong>diti<strong>on</strong>ed <strong>on</strong> delay<br />
The estimated expected power of multipath comp<strong>on</strong>ents c<strong>on</strong>diti<strong>on</strong>ed <strong>on</strong> delays can be obtained from<br />
(4.17) as:<br />
{ } ( )<br />
E p| τ ∝ P τ / f( τ)<br />
. (4.22)<br />
In order to make the c<strong>on</strong>cept of the generic <strong>channel</strong> model approach clear, we can think of the case when<br />
both P ( τ ) <strong>and</strong> f ( τ ) are exp<strong>on</strong>ential decaying functi<strong>on</strong>s. The <strong>on</strong>e-side exp<strong>on</strong>ential decaying functi<strong>on</strong><br />
P τ is expressed as:<br />
that describes the ( )<br />
where<br />
P<br />
( τ )<br />
( −τ<br />
σ ),<br />
⎧ exp<br />
τ<br />
for τ > 0<br />
⎪<br />
∝ ⎨<br />
⎪<br />
⎩0,<br />
otherwise<br />
(4.23)<br />
σ<br />
τ is the RMS delay spread. The exp<strong>on</strong>ential functi<strong>on</strong> that describes the probability density<br />
f τ is expressed as:<br />
f τ ∝ exp −τ<br />
~<br />
, (4.24)<br />
functi<strong>on</strong> of the delays ( )<br />
where<br />
σ ~<br />
τ<br />
( ) ( )<br />
is st<strong>and</strong>ard deviati<strong>on</strong> of the path delays. Hence, the expected power c<strong>on</strong>diti<strong>on</strong>ed <strong>on</strong> delay<br />
(4.25) can be written as:<br />
Now, let us define a parameter r τ as follows:<br />
use (4.26) in (4.25), we get:<br />
σ τ<br />
⎛ σ%<br />
τ<br />
−σ<br />
⎞<br />
τ<br />
Pn<br />
= E{ p| τ}<br />
∝exp<br />
⎜−τ ⎟. (4.25)<br />
⎝ σσ %<br />
τ τ ⎠<br />
σ ~<br />
τ<br />
r<br />
τ<br />
=<br />
(4.26)<br />
στ<br />
⎛ rτ<br />
−1⎞<br />
Pn<br />
= E{ p| τ}<br />
∝exp⎜−τ<br />
⎟<br />
⎝ rτσ<br />
τ ⎠ . (4.27)<br />
Thus, the expected power of multipath comp<strong>on</strong>ents c<strong>on</strong>diti<strong>on</strong>ed <strong>on</strong> delay depends <strong>on</strong> the RMS delay<br />
spread <strong>and</strong> the parameter that describes the ratio between the path delays st<strong>and</strong>ard deviati<strong>on</strong> <strong>and</strong> the RMS<br />
delay spread.<br />
For the case when P ( τ ) is exp<strong>on</strong>ential as in (4.24) <strong>and</strong> the f ( τ ) is uniform U ( 0,<br />
τ )<br />
power c<strong>on</strong>diti<strong>on</strong>ed <strong>on</strong> delay (4.22) can be written as:<br />
Pn<br />
4.1.4.1.2 The power azimuth-delay spectrum<br />
max<br />
, the expected<br />
⎛ τ ⎞<br />
= E{ p| τ}<br />
∝exp⎜−<br />
⎟<br />
⎝ στ<br />
⎠ , τ ≤ τ<br />
max<br />
(4.28)<br />
We will focus our discussi<strong>on</strong> <strong>on</strong> azimuth angles at both transmitter <strong>and</strong> receiver. Now, we call the double-<br />
P φ , ϕ,<br />
τ <strong>and</strong> its corresp<strong>on</strong>ding<br />
directi<strong>on</strong>al-delay spectrum as the double-azimuth-delay spectrum, i.e., ( )<br />
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pdf as the double-azimuth-delay probability density functi<strong>on</strong> f ( φ , ϕ,<br />
τ ). The joint functi<strong>on</strong>s f ( φ , ϕ,<br />
τ )<br />
<strong>and</strong> P ( φ , ϕ,<br />
τ ) are mathematically intractable as it is a joint distributi<strong>on</strong> of n<strong>on</strong>-Gaussian r<strong>and</strong>om<br />
variables. Hence, we can study the power azimuth-angle-delay spectrum, P ( φ , ϕ,<br />
τ ). Under the<br />
assumpti<strong>on</strong> that the power spectrum functi<strong>on</strong> of <strong>on</strong>e domain is independent of partial informati<strong>on</strong> in other<br />
domains, if ∆τ<br />
is small enough such that the RMS delay spread of multipath comp<strong>on</strong>ents within ∆ τ is<br />
very small <strong>and</strong> close to zero, while they are separated in azimuth-departure angle domain, we call P ~ ( φ)<br />
as the zero-delay-spread cluster of departure (ZDSC_D). This defines cluster characteristics of multipath<br />
comp<strong>on</strong>ents that are separated in azimuth angle of departure domain but have almost same delays.<br />
Similarly with the power azimuth-arrival-angle-delay spectrum such that having the same argument about<br />
having ∆ τ small enough such that the RMS delay spread of multipath comp<strong>on</strong>ents within ∆ τ is very<br />
small <strong>and</strong> close to zero while they are separated in azimuth-arrival angles domain, we call P ~ ( ϕ)<br />
as the<br />
zero-delay-spread cluster of arrival (ZDSC_A). This defines cluster characteristics of multipath<br />
comp<strong>on</strong>ents that are separated in azimuth-angle of arrival domain <strong>and</strong> have almost same delay. With the<br />
argument discussed above we can say that:<br />
4.1.4.1.3 Large-scale parameters<br />
P<br />
( φ ϕ,<br />
τ ) ∝ P( φ) P( ϕ) P( τ )<br />
P is a functi<strong>on</strong> of the RMS angle-<br />
It is known that P ( φ)<br />
is a functi<strong>on</strong> of the RMS angle-spreadσ φ , ( ϕ)<br />
spreadσ , <strong>and</strong> ( τ )<br />
, (4.29)<br />
ϕ P is a functi<strong>on</strong> of the RMS delay spreadσ τ . For each power departure-azimuthdelay<br />
spectrum <strong>and</strong> power arrival-azimuth-delay spectrum that represents a specific <strong>channel</strong> segment, the<br />
sets ( σ , σ , σ ) are c<strong>on</strong>sidered fixed but they change from <strong>channel</strong> segment to another with movement<br />
φ<br />
τ<br />
ϕ<br />
of the MS. Hence, these sets can be c<strong>on</strong>sidered as r<strong>and</strong>om variables. Therefore, they can be described by<br />
f σ , σ , σ . The marginal probability density functi<strong>on</strong> of each<br />
joint probability density functi<strong>on</strong> as ( )<br />
dispersi<strong>on</strong> metric can be obtained as:<br />
f<br />
f<br />
f<br />
ϕ<br />
φ<br />
τ<br />
( στ<br />
) = ∫∫ f ( σ<br />
ϕ<br />
σ<br />
φ<br />
, στ<br />
) dσφdσ<br />
ϕ<br />
( σ<br />
φ<br />
) = ∫∫ f ( σ<br />
ϕ<br />
σ<br />
φ<br />
, στ<br />
) dστdσ<br />
ϕ<br />
( σ<br />
ϕ<br />
) = ∫∫ f ( σϕ<br />
σ<br />
φ<br />
, στ<br />
) dστdσ<br />
ϕ<br />
, (4.30)<br />
, (4.31)<br />
, (4.32)<br />
In literature the distributi<strong>on</strong>s of these parameters are usually <str<strong>on</strong>g>report</str<strong>on</strong>g>ed as lognormal for some of outdoor<br />
scenarios. To represent the <strong>channel</strong> characteristics, the sets ( σ , σ , σ ) must be selected r<strong>and</strong>omly<br />
while c<strong>on</strong>sidering the correlati<strong>on</strong> between them to represent their <strong>channel</strong> segment.<br />
4.1.4.1.4 Bulk parameter cross-correlati<strong>on</strong><br />
Generati<strong>on</strong> of multipath comp<strong>on</strong>ent characteristics in teRMS of rays parameters, i.e., delays, angle of<br />
departures <strong>and</strong> angle of arrivals are drawn from r<strong>and</strong>om number generators specified by probability<br />
density functi<strong>on</strong>s of the corresp<strong>on</strong>ding parameters by combining the Gaussian distributi<strong>on</strong> with the<br />
transformati<strong>on</strong> functi<strong>on</strong> g ( x)<br />
, see Secti<strong>on</strong> 4.1.4.2 below. These distributi<strong>on</strong>s are functi<strong>on</strong>s of dispersi<strong>on</strong><br />
metrics that are discussed in Secti<strong>on</strong> 3.1.1 earlier. These dispersi<strong>on</strong> metrics might be correlated with each<br />
other, with lognormal shadowing <strong>and</strong> cross-polarisati<strong>on</strong> ratio. Thus, correlati<strong>on</strong> has to be c<strong>on</strong>sidered in<br />
generati<strong>on</strong> of dispersi<strong>on</strong> metric <strong>and</strong> shadowing. For each <strong>link</strong>, the correlati<strong>on</strong>s between all large-scale<br />
parameters are taken into account. In additi<strong>on</strong>, the correlati<strong>on</strong> of these parameters between two MS <strong>and</strong><br />
<strong>on</strong>e BS (or <strong>on</strong>e MS at two points in time) are modelled by c<strong>on</strong>sidering the auto-correlati<strong>on</strong> properties of<br />
the large-scale parameters. However, the cross-correlati<strong>on</strong> in the <strong>link</strong>s between <strong>on</strong>e MS <strong>and</strong> two BS are<br />
set to zero in this model based <strong>on</strong> the discussi<strong>on</strong> in Secti<strong>on</strong> 4.1.4.2.3.<br />
4.1.4.1.5 Azimuth angle distributi<strong>on</strong>s of ZDSC<br />
The mean departure angle of ZDSC_D <strong>and</strong> mean arrival angle of ZDSC_A can be located anywhere<br />
within the azimuth-departure-angle domain or azimuth-arrival-angle domain. The departure (arrival)<br />
angles of the rays within the ZDSC_D (ZDSC_A) are generated to satisfy certain angle-spreads within the<br />
cluster. In order to reduce the complexity of the <strong>channel</strong> model the same angle-spreads of all ZDSC is<br />
assumed. These angle-spreads may vary from scenario to another. In order to minimize the model<br />
complexity, the angle spacing between rays within the cluster is c<strong>on</strong>sidered fixed to satisfy a specific<br />
angle-spread. The azimuth angles spacing of rays is predefined as an offset from a mean angle of the<br />
φ<br />
τ<br />
ϕ<br />
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cluster (ZDSC). These mean angles of the ZDSCs are generated by r<strong>and</strong>om generators of defined<br />
probability density functi<strong>on</strong>s. The probability density functi<strong>on</strong>s of azimuth angles of ZDSC of either<br />
departure or arrival are denoted as f ( φ)<br />
<strong>and</strong> f ( ϕ)<br />
, respectively, are independent of their delays. When<br />
the pdf of ZDSC_A <strong>and</strong> ZDSC_D are zero mean truncated Gaussian, they can be written as<br />
where Ψ =<br />
<strong>and</strong><br />
where Ω =<br />
Here<br />
σ ~ φ <strong>and</strong><br />
1<br />
2πσ~<br />
1<br />
2πσ~<br />
ϕ<br />
φ<br />
π<br />
∫<br />
−π<br />
π<br />
∫<br />
−π<br />
⎛<br />
2<br />
⎜<br />
ϕ<br />
exp<br />
−<br />
⎝ 2 σ ~<br />
2<br />
⎛ φ<br />
exp<br />
⎜ −<br />
⎝ 2 σ ~<br />
⎛<br />
2<br />
( ) = 1<br />
⎞<br />
⎜<br />
ϕ<br />
f ϕ<br />
exp − ⎟<br />
2 ~ Ψ<br />
⎝ 2 σ ~ 2<br />
(4.33)<br />
πσ<br />
ϕ ϕ ⎠<br />
2<br />
ϕ<br />
⎞<br />
⎟<br />
dϕ<br />
,<br />
⎠<br />
⎛<br />
2<br />
( ) = 1<br />
⎞<br />
⎜<br />
φ<br />
f φ<br />
exp − ⎟<br />
2 ~ Ω<br />
⎝ 2 σ ~ 2<br />
(4.34)<br />
πσ<br />
φ φ ⎠<br />
2<br />
⎞<br />
⎟dφ<br />
⎠<br />
σ ~ ϕ are st<strong>and</strong>ard deviati<strong>on</strong>s <strong>and</strong> are related to the RMS angle-spreads σ<br />
φ <strong>and</strong><br />
σ<br />
ϕ represent the composite RMS angle-<br />
the parameter r φ <strong>and</strong> r ϕ , respectively. Note that the<br />
spreads not per cluster angle-spreads (AS).<br />
4.1.4.1.6 Impulse resp<strong>on</strong>se of ZDSC<br />
σ<br />
φ <strong>and</strong><br />
σ<br />
ϕ through<br />
With very wide b<strong>and</strong>width, fading of comp<strong>on</strong>ent of certain delays is due to interference between<br />
multipath comp<strong>on</strong>ents that arrive in clusters having same or very close delays but differ in angle of<br />
arrivals <strong>and</strong>/or angle of departures. This has been discussed previously <strong>and</strong> represents the c<strong>on</strong>cept of<br />
ZDSC. Having the c<strong>on</strong>cept of ZDSC, the functi<strong>on</strong> (D 3 SF) can be written as:<br />
N M<br />
, , , , , , , ,<br />
( τφθϕϑ) = ∑∑ αnm , ( t) δ ( φ−φnm ,<br />
θ −θnm ,<br />
ϕ −ϕnm ,<br />
ϑ−ϑnm , ) δ ( τ −τn)<br />
ht<br />
n= 1 m=<br />
1<br />
(4.35)<br />
where N is number of ZDSCs, <strong>and</strong> M is the number of rays within the cluster. Here in (4.37), we assume<br />
same number of rays in each ZDSC. The spacing in angle domain between rays around mean angle of the<br />
cluster is determined to satisfy certain angle-spread of certain power azimuth spectrum. The power<br />
divisi<strong>on</strong> between rays of total cluster power could be dependent of angle of arrival (departure) or same<br />
power in all rays can also be assumed. For the case when equal power between rays is assumed, the<br />
angles are separated based <strong>on</strong> certain PAS. One widely used PAS is the Laplacian power spectrum, the<br />
power of each ray is P n<br />
M , where P<br />
n is the power of the nth cluster, the departure or arrival angles are<br />
spaced n<strong>on</strong>-uniformly to based <strong>on</strong> Laplacian PAS.<br />
4.1.4.2 Correlati<strong>on</strong> of large-scale parameters between <strong>link</strong>s<br />
In the generic WINNER model, large-scale parameters give a higher-<strong>level</strong> characterizati<strong>on</strong> of the<br />
propagati<strong>on</strong> <strong>channel</strong>. These parameters are treated as r<strong>and</strong>om variables <strong>on</strong> a <strong>channel</strong> segment basis. They<br />
are r<strong>and</strong>omized in a first step – <strong>and</strong> <strong>on</strong>ly there-after - are the detailed parameters of the <strong>channel</strong> model<br />
being r<strong>and</strong>omized using these large-scale parameters as input.<br />
The following large-scale parameters may be c<strong>on</strong>sidered (currently <strong>on</strong>ly the first 6 are actually used)<br />
1. Delay-spread<br />
2. AoD angle-spread<br />
3. AoA angle-spread<br />
4. Shadow fading<br />
5. AoD elevati<strong>on</strong> spread.<br />
6. AoA elevati<strong>on</strong> spread.<br />
7. Cross polarisati<strong>on</strong> ratio 1.<br />
8. Cross polarisati<strong>on</strong> ratio 2.<br />
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Depending <strong>on</strong> the measurement capabilities <strong>and</strong> the scenario requirements some of the parameters may be<br />
neglected. Let us denote the vector of these elements s ( x, y)<br />
where we think of s( x, y)<br />
as a stochastic<br />
s x, y be m .We call<br />
multivariate process where x <strong>and</strong> y is the positi<strong>on</strong> of the user. Let the size of ( )<br />
s ( x, y)<br />
“the large-scale parameter vector” or “large-scale vector” for short.<br />
The angles in ( x, y)<br />
unit-less (i.e. not in dB). The value of s ( x, y)<br />
in two positi<strong>on</strong>s s ( ) <strong>and</strong> s(<br />
)<br />
s are taken to be degrees, the delay-spread in sec<strong>on</strong>ds, <strong>and</strong> the remaining <strong>on</strong>es are<br />
x , y x , y 1 1<br />
2 2 can be used to<br />
represent two users or the same user which is at the two positi<strong>on</strong>s at two different time instants. When<br />
1<br />
2<br />
two base-stati<strong>on</strong> sites are involved, two different vectors s ( x, y)<br />
<strong>and</strong> s ( x, y)<br />
characterize the <strong>link</strong> to the<br />
two sites <strong>and</strong> the positi<strong>on</strong> x, y , respectively.<br />
In the first three secti<strong>on</strong>s following, we further elaborate this model in teRMS of distributi<strong>on</strong>, autocorrelati<strong>on</strong>,<br />
<strong>and</strong> inter-base-stati<strong>on</strong> correlati<strong>on</strong>. In the fourth secti<strong>on</strong> we describe how an evolving <strong>channel</strong><br />
can be generated based <strong>on</strong> the model.<br />
The model obtained here is similar to the ideas in [Alg02]. Here however, we c<strong>on</strong>sider <strong>system</strong> <strong>level</strong><br />
variables with different correlati<strong>on</strong> distances as well as n<strong>on</strong> log-normal variables. We also analyze crosscorrelati<strong>on</strong><br />
functi<strong>on</strong>s an issue which is completely overlooked in [Alg02]. Furthermore, we discuss<br />
extensi<strong>on</strong> to include inter-site correlati<strong>on</strong>s in Secti<strong>on</strong> 4.1.4.2.3 below.<br />
4.1.4.2.1 Distributi<strong>on</strong>s<br />
Based <strong>on</strong> measurements <strong>and</strong> literature, we have found transfoRMS g ( s)<br />
for each element of ( x, y)<br />
~<br />
that the transformed vector ( x , y) g( s( x,<br />
y)<br />
)<br />
s such<br />
s = is a vector of Gaussian r<strong>and</strong>om variables for each scenario.<br />
We assume that the elements of this vector are jointly Gaussian. We have also found the inverse<br />
transform, such that we can easily generate samples of the distributi<strong>on</strong> by generating a vector of r<strong>and</strong>om<br />
−<br />
s x , y = g<br />
1 ~<br />
s x,<br />
y .<br />
variables <strong>and</strong> then perform the inverse i.e. ( ) ( ( ))<br />
4.1.4.2.2 Auto-correlati<strong>on</strong>s<br />
In order to facilitate generati<strong>on</strong> of multiple realizati<strong>on</strong>s of ~ s ( x, y)<br />
in several<br />
positi<strong>on</strong>s x = x , y = y 1 1 , x = x , y = y<br />
~ 2 2 , … need to know the mean <strong>and</strong> the auto-correlati<strong>on</strong> of the vector<br />
s ( x, y)<br />
i.e.<br />
= {<br />
~ s ( x, y)<br />
} , R( r ) = Ε (<br />
~<br />
s( x , y ) − µ )( ~<br />
s( x y ) − µ )<br />
µ Ε<br />
{ }<br />
T<br />
∆<br />
1 1<br />
0,<br />
0<br />
2<br />
, where ( ) ( ) 2<br />
∆ r = x<br />
, (4.36)<br />
1 − x0<br />
+ y1<br />
− y0<br />
where we have assumed that the autocorrelati<strong>on</strong> is <strong>on</strong>ly a functi<strong>on</strong> of the distance between any two points.<br />
The correlati<strong>on</strong> matrix c<strong>on</strong>tains the cross-correlati<strong>on</strong> functi<strong>on</strong>s between all element variables. In order to<br />
arrive at a model which is usable also when simulating cases involving many positi<strong>on</strong>s we have impose a<br />
structure <strong>on</strong> R ( ∆r)<br />
namely<br />
R<br />
⎛<br />
⎜<br />
⎝<br />
⎛<br />
⎜<br />
⎝<br />
∆r<br />
⎞ ⎛ ∆r<br />
⎞⎞<br />
⎟<br />
K<br />
⎜ ⎟⎟<br />
(*) (4.37)<br />
λ1<br />
⎠ ⎝ λm<br />
⎠⎠<br />
0.5<br />
0.5,T<br />
( ∆r) = R ( 0) diag⎜exp⎜−<br />
⎟,<br />
,exp⎜−<br />
⎟⎟R<br />
( 0)<br />
0.5<br />
T 0.5<br />
5<br />
where R ( 0)<br />
is obtained from the eigendecompositi<strong>on</strong> R( 0) = EΛE<br />
as R ( 0) = EΛ<br />
0. .<br />
Using a model with this structure we can simulate realizati<strong>on</strong>s of ( x, y)<br />
m independent Gaussian r<strong>and</strong>om processes, ?( x, y) [ ξ ( x,<br />
y) ( x y)<br />
] T<br />
1<br />
Kξ<br />
m<br />
,<br />
variance <strong>on</strong>e. The autocorrelati<strong>on</strong> of process ξc ( x,<br />
y)<br />
is given by exp( − ∆r / λ c<br />
)<br />
dimensi<strong>on</strong>al “maps” can be performed with a filtering operati<strong>on</strong>. Following the generati<strong>on</strong> of ( x, y)<br />
transformed large-scale vector is obtained as<br />
~ s by first generating<br />
= , each <strong>on</strong>e with mean zero,<br />
0.<br />
( x,<br />
y) = R ( ∆r) ?( x y) + µ<br />
~ 5<br />
s ,<br />
. Generating such two<br />
? the<br />
. (4.38)<br />
Thus we have in Secti<strong>on</strong> fitted model parameters µ <strong>and</strong>λ , K,λ<br />
to our measurements.<br />
Note that the resulting effective autocorrelati<strong>on</strong> functi<strong>on</strong> for each large-scale parameter is not exp<strong>on</strong>ential,<br />
as comm<strong>on</strong>ly found in literature, but rather a sum of weighted exp<strong>on</strong>ential functi<strong>on</strong>s.<br />
4.1.4.2.3 Multi-site cross-correlati<strong>on</strong>s<br />
The justificati<strong>on</strong> for introducing the cross-correlati<strong>on</strong>s of the large-scale parameters is that the <strong>link</strong>s<br />
between a pair of base-stati<strong>on</strong>s <strong>and</strong> a mobile-stati<strong>on</strong> is that 1) there may be many comm<strong>on</strong> scatterers in<br />
the close proximity of the MS 2) comm<strong>on</strong> shadowing objects of two mobiles located close in angle <strong>and</strong> 3)<br />
1<br />
m<br />
Page 49 (167)
WINNER D5.4 v. 1.4<br />
cell sub-areas with different local propagati<strong>on</strong> characteristics as indicated in Figure 4.1, Figure 4.2 <strong>and</strong><br />
Figure 4.3, respectively. In the example of Figure 4.2 the correlati<strong>on</strong> could arise from the obstructi<strong>on</strong> of<br />
the same building while in Figure 4.3 the correlati<strong>on</strong> arises from the fact that the local envir<strong>on</strong>ment of the<br />
mobile stati<strong>on</strong> is the same for both base-stati<strong>on</strong>s. In the examples of Figure 4.1<strong>and</strong> Figure 4.2 it is<br />
reas<strong>on</strong>able that the correlati<strong>on</strong> would increase when the angle β between the two base-stati<strong>on</strong>s seen at the<br />
mobile stati<strong>on</strong>, see Figure 4.4. Such a dependence correlati<strong>on</strong> has been observed in [Maw92] where the<br />
shadow-fading is investigated.<br />
X<br />
BS<br />
MS<br />
X<br />
BS<br />
Figure 4.1: Links to two base-stati<strong>on</strong>s with comm<strong>on</strong> scatterers.<br />
BS<br />
BS<br />
MS<br />
Figure 4.2: Shadowing by the same object in the two <strong>link</strong>s of two different base-stati<strong>on</strong>s.<br />
BS<br />
A<br />
B<br />
C<br />
BS<br />
Figure 4.3: Two base-stati<strong>on</strong>s with three local areas A, B <strong>and</strong> C which are characterized as A) open<br />
green-field, B) wooded area C) Built-up area.<br />
BS1<br />
r 1 r 2<br />
B<br />
BS2<br />
h 1<br />
h<br />
2<br />
MS<br />
Figure 4.4: The geometry of two base-stati<strong>on</strong>s <strong>and</strong> a mobile-stati<strong>on</strong>.<br />
Page 50 (167)
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A measurement campaign has been c<strong>on</strong>ducted in a metropolitan typical urban macro-cell (scenario C2),<br />
with simultaneous measurements of the three <strong>link</strong>s between a single mobile-stati<strong>on</strong>, <strong>and</strong> three sector<br />
antenna arrays, <strong>on</strong>e of them located just above average rooftop <strong>level</strong> <strong>and</strong> the other two sectors well above<br />
roof-top <strong>level</strong>, see Secti<strong>on</strong> 5.2.5. No correlati<strong>on</strong> was found between the two sites in the measurements.<br />
However, this is believed to be due to measurement problems <strong>and</strong> we believe the true correlati<strong>on</strong> between<br />
sectors of the same cell is full. The geometry of the measurements where such that the angle β between<br />
the base-stati<strong>on</strong>s was typically large as illustrated in Figure 4.4. In additi<strong>on</strong> the base-stati<strong>on</strong> heights were<br />
different. These measurements show that for metropolitan typical urban envir<strong>on</strong>ments (scenario C2)<br />
under the c<strong>on</strong>diti<strong>on</strong>s stated the correlati<strong>on</strong> is zero.<br />
In the future it is possible to develop <strong>models</strong> the correlati<strong>on</strong> is situati<strong>on</strong>s where it exists. It is likely that it<br />
<strong>on</strong>ly exist in a few situati<strong>on</strong>s when β is small <strong>and</strong> the distances r 1 <strong>and</strong> r2<br />
are close. This situati<strong>on</strong> would<br />
actually be preferable from the point of view of complexity of the <strong>channel</strong> generati<strong>on</strong> in simulati<strong>on</strong>s. A<br />
computati<strong>on</strong>ally efficient way of introducing such correlati<strong>on</strong>s is described by the following two<br />
equati<strong>on</strong>s<br />
<strong>and</strong><br />
( ) µ<br />
0.5<br />
( x,<br />
y) = ( ∆r) ? ( x,<br />
y) + ( x y)<br />
~ 1<br />
1<br />
s R ξ , +<br />
(4.39)<br />
( ) µ<br />
0.5<br />
( x,<br />
y) = ( ∆r) ? ( x,<br />
y) + ( x y)<br />
~ 2<br />
2<br />
s R ξ , + , respectively, (4.40)<br />
1<br />
2<br />
where the r<strong>and</strong>om processes ξ ( x, y)<br />
, ? ( x, y)<br />
<strong>and</strong> ( x, y)<br />
~1 ~2<br />
s ( x,<br />
y)<br />
<strong>and</strong> s ( x,<br />
y)<br />
is introduced by the comm<strong>on</strong> r<strong>and</strong>om process ξ ( x, y)<br />
T<br />
{?<br />
( x y) ? ( x,<br />
y)<br />
} = diag( c , K,<br />
)<br />
<strong>and</strong><br />
,<br />
1<br />
? are independent <strong>and</strong> the correlati<strong>on</strong> between<br />
c m<br />
.Assuming<br />
E (4.41)<br />
1 1,T<br />
2 2,T<br />
{?<br />
( x y) ? ( x,<br />
y)<br />
} E ? ( x,<br />
y) ? ( x,<br />
y)<br />
{ } = diag( 1- c , K,<br />
− )<br />
E = 1 , (4.42)<br />
this produces a cross-correlati<strong>on</strong> of the form<br />
E<br />
,<br />
1<br />
{ ~ 1 2, T<br />
0.5<br />
0.5, T<br />
s ( x,<br />
y) ~ s ( x,<br />
y)<br />
} = ( ∆r) diag( c , , c ) R ( ∆r)<br />
R K . (4.43)<br />
A key questi<strong>on</strong> here is whether this correlati<strong>on</strong> structure <strong>models</strong> the true cross-correlati<strong>on</strong> accurately<br />
enough or not. In the multi-cell measurements available now correlati<strong>on</strong> was found between sites, see<br />
Secti<strong>on</strong>s 5.2.5.1 <strong>and</strong> 9.3.1. For this reas<strong>on</strong> the <strong>system</strong> <strong>level</strong> vectors of different sites will be modeled as<br />
independent.<br />
In the WINNER model as well as in SCM the shadow-fading is correlated with a correlati<strong>on</strong> coefficient<br />
of 0.5. The <strong>on</strong>ly reference known to the authors where inter-site correlati<strong>on</strong> has been studied is [Maw92].<br />
In this paper the cross-correlati<strong>on</strong> between the log-normal-fading is approximated as 0.9<br />
− θ / 200 where<br />
θ is the angle between the two sites <strong>and</strong> seen from the MS. The model is based <strong>on</strong> measurements two<br />
base-stati<strong>on</strong>s at 45 <strong>and</strong> 90 meter height, the distance to the two base-stati<strong>on</strong>s are 2-38 km <strong>and</strong> the<br />
frequencies are in the 154-922 MHz range. These parameters are substantially different from the<br />
measurements made in the WINNER project <strong>and</strong> we therefore chose to put neglect them.<br />
4.2 Stati<strong>on</strong>ary-feeder scenarios B5<br />
It should be noted that for the other prioritized scenarios WP5 have measurement data <strong>and</strong> improved<br />
modelling of them is an <strong>on</strong>going effort while for the feeder scenarios this is not the case. In any case WP5<br />
feels that the interim <strong>channel</strong> <strong>models</strong> are sufficiently good as a starting point for simulati<strong>on</strong>s <strong>and</strong> further<br />
discussi<strong>on</strong>s. In stati<strong>on</strong>ary feeder both terminals are fixed, the <strong>channel</strong> modelling approach is clustered<br />
delay-line modelling approach. Clustered delay line <strong>models</strong> for the first two sub-scenarios (B5a <strong>and</strong> B5b),<br />
defined in Subsecti<strong>on</strong> 1.3.2.1.3, are defined in Secti<strong>on</strong> 4 based <strong>on</strong> the analysis below <strong>and</strong> we describe how<br />
<strong>models</strong> for the B5c <strong>and</strong> B5d sub-scenarios could be obtained by modifying scenario B1 <strong>and</strong> C2,<br />
respectively.<br />
4.2.1 B5a LOS stati<strong>on</strong>ary feeder: rooftop-to-rooftop<br />
The propagati<strong>on</strong> scenario in the LOS measurements in [PT00] <strong>and</strong> [SCK05] is the most similar to ours,<br />
although in [SCK05] the distance is very short. Therefore, we will base the propagati<strong>on</strong> model mostly <strong>on</strong><br />
[PT00] for the parameters that are available from in that paper. Remaining parameters will be derived<br />
from the other papers. Only a tapped delay-line model is provided. This scenario probably can be<br />
characterized by a str<strong>on</strong>g line of sight <strong>and</strong> single-bounce reflecti<strong>on</strong> as indicated in Figure 4.5. If the LOS<br />
path is slightly obstructed, the influence of multi-path-related parameters will be str<strong>on</strong>g, <strong>and</strong> far-away<br />
1<br />
m<br />
c m<br />
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reflecti<strong>on</strong>s can be expected due to the free-space c<strong>on</strong>diti<strong>on</strong>s from/to the reflectors. Due to lack of<br />
measurements we will use fixed angle-spread, delay-spread <strong>and</strong> XPR-value. L<strong>on</strong>g <strong>and</strong> short-term fading<br />
will be used however. Thus <strong>on</strong>ly a tapped delay-line model will be provided for this scenario. Note that<br />
due to the single-bounce nature of the propagati<strong>on</strong>, directive antennas are very effective in reducing<br />
delay-spread <strong>and</strong> other impacts of multi-path, see e.g. the delay-spread reducti<strong>on</strong> in [PT00]. We use the<br />
RMS-delay-spread value of 40 ns. This is the largest value observed in a measurement campaign which<br />
utilized antennas with 53 degrees <strong>and</strong> 10 degree opening angles in the <strong>link</strong>-ends, see [PT00]. This is also<br />
close to the median RMS-delay-spread with basically omni-directi<strong>on</strong>al antennas measured in [OBL+02]<br />
but somewhat larger than in [SCK05] but the measurements in [SCK05] are at very short range.<br />
Therefore, the model should be understood such that it is applicable using omni-directi<strong>on</strong>al antennas for<br />
up to 300meters distance, while beam-widths comparable or narrower that the aforementi<strong>on</strong>ed 10/53<br />
degrees should be used at larger distances.<br />
X<br />
X<br />
Figure 4.5: Single Bounce Reflecti<strong>on</strong> Model<br />
4.2.2 B5b LOS stati<strong>on</strong>ary feeder: street-<strong>level</strong> to street-<strong>level</strong><br />
The measurement campaigns listed in the literature review are performed at very different frequencies: all<br />
the way from 2 to 10GHz. However, in papers e.g. [Bal02], [SBA+02] the results for different carried<br />
frequencies are very similar. Therefore we chose to disregard the difference in frequency for this interim<br />
<strong>channel</strong> model. The principle adopted for the WINNER <strong>models</strong> allows for various correlati<strong>on</strong>s between<br />
different parameters such as angle-spread, shadow-fading <strong>and</strong> delay-spread. We will use <strong>on</strong>e such<br />
dependence namely that in [MKA02], which dependence is between path loss <strong>and</strong> delay-spread. For B5b<br />
however, <strong>on</strong>ly Clustered-delay-line <strong>models</strong> will be provided (CDL) <strong>and</strong> the dependence between path loss<br />
<strong>and</strong> delay-spread is h<strong>and</strong>led by selecting <strong>on</strong>e of three different CDL <strong>models</strong>.<br />
Our underst<strong>and</strong>ing of the scenario is that both the transmitter <strong>and</strong> receivers have many scatterers in their<br />
close vicinity similar as theorized in [Sva02]. In additi<strong>on</strong> there can also be l<strong>on</strong>g echoes from the ends of<br />
the street. However, there is a line-of-sight ray between the transmitter <strong>and</strong> receiver. When this path is<br />
str<strong>on</strong>g the c<strong>on</strong>tributi<strong>on</strong> from all the scatters is small <strong>and</strong> therefore also all the different foRMS of<br />
dispersi<strong>on</strong>. However, bey<strong>on</strong>d the breakpoint distance the scatterers start to play an important role. Based<br />
<strong>on</strong> the BS <strong>and</strong> MS height of most references we assume that model is valid for 2-5 meter access point<br />
heights. A clustered delay-line model with the properties given below is defined. The parameters <strong>and</strong> their<br />
motivati<strong>on</strong>s are as follows.<br />
4.2.3 B5c hotspot LOS stati<strong>on</strong>ary-feeder: below rooftop to street-<strong>level</strong>.<br />
This can be modelled identical to the LOS versi<strong>on</strong> of the B1 model except that support for the Doppler<br />
spectrum of stati<strong>on</strong>ary cases has to be introduced. How to support higher feeder peripheral stati<strong>on</strong><br />
antennas than typical mobile-stati<strong>on</strong> heights has not been c<strong>on</strong>sidered yet.<br />
We propose the introducti<strong>on</strong> of individual Doppler frequencies similar to the model in [TPE02]. The<br />
Doppler frequency will not be a functi<strong>on</strong> of the AoA at the receiver since the <strong>channel</strong> variati<strong>on</strong> is not due<br />
to temporal variati<strong>on</strong>s of the <strong>channel</strong> in fixed applicati<strong>on</strong>s. We select the Doppler model of [Erc01],<br />
which has somewhat higher Doppler spread than [DGM+03] probably due to the influence of traffic.<br />
4.2.4 B5d hotspot NLOS stati<strong>on</strong>ary feeder: rooftop to street-<strong>level</strong>.<br />
This model is based <strong>on</strong> C2 model except the Doppler spectrum <strong>and</strong> an additi<strong>on</strong>al term in the path-loss<br />
model. The Doppler spectrum can be h<strong>and</strong>led as in B5c. To support higher heights of the feeder<br />
peripheral stati<strong>on</strong>s than in the C2 model, a compensati<strong>on</strong> term is introduced. We have investigated the<br />
term based <strong>on</strong> the Cost 231 Walfish-Ikegami, Walfish-Bert<strong>on</strong>i <strong>and</strong> Hata-<strong>models</strong> [Cost231], [MBX94],<br />
[Hat80] for the scenario depicted in Figure 3-2. We have set the parameters to w =30meter, x=w/2,<br />
h = h +10<br />
b B . The results for h<br />
B<br />
=12, 18 <strong>and</strong> 24 meter are shown in Figure 4.7. As a comprise between<br />
these curves we propose a gain from using a higher MS antenna than 0.1meter as<br />
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Heigh_Gain dB<br />
= 0.7h m<br />
(4.44)<br />
where the gain is in dB <strong>and</strong> the height h<br />
m is in meters. The Doppler modelling is made identical to that of<br />
B5c.<br />
Figure 4.6: Illustrati<strong>on</strong> of the c<strong>on</strong>sidered scenario in B5 NLOS stati<strong>on</strong>ary feeder: rooftop to street<strong>level</strong>.<br />
Figure 4.7: Compensati<strong>on</strong> term.<br />
4.3 Coefficient generati<strong>on</strong> approaches<br />
We have selected two st<strong>and</strong>ardized spatial <strong>channel</strong> <strong>models</strong> as <strong>channel</strong> <strong>models</strong> for initial usage [D5.1],<br />
namely the 3GPP SCM <strong>and</strong> the IEEE 802.11n model. The former model is <strong>on</strong>ly for outdoor <strong>and</strong> the latter<br />
is <strong>on</strong>ly for indoor scenarios. Interestingly, these st<strong>and</strong>ards also represent two comm<strong>on</strong> but different<br />
approaches to coefficient generati<strong>on</strong>. In the following, we will evaluate the individual advantages <strong>and</strong><br />
disadvantages of these approaches. We also examine the issue of creating a model that is not restricted to<br />
Kr<strong>on</strong>ecker type spatial correlati<strong>on</strong>.<br />
4.3.1 Stati<strong>on</strong>ary stochastic<br />
Example: ETSI BRAN HIPERLAN/2, IEEE 802.11n<br />
Advantages:<br />
• Efficient coefficient generati<strong>on</strong> by correlati<strong>on</strong> of r<strong>and</strong>om variables.<br />
Disadvantages:<br />
• Calculati<strong>on</strong> of spatial autocorrelati<strong>on</strong> functi<strong>on</strong>s requires numerical integrati<strong>on</strong>.<br />
• Two-dimensi<strong>on</strong>al filtering across antenna elements <strong>and</strong> time required.<br />
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• Model approach is by default stati<strong>on</strong>ary. Modelling of n<strong>on</strong>-stati<strong>on</strong>ary effects requires extensi<strong>on</strong>.<br />
4.3.2 Sum-of-Sinusoids<br />
Example: Jakes’ fading generator, 3GPP SCM<br />
Advantages:<br />
• Correlati<strong>on</strong> across antenna elements <strong>and</strong> time created implicitly.<br />
• N<strong>on</strong>-stati<strong>on</strong>ary processes potentially easier to integrate.<br />
Disadvantages:<br />
• Requires a large number of sinusoids for realistic modelling <strong>and</strong> thus computati<strong>on</strong>ally expensive.<br />
• Resulting Doppler spectra are peaky (with number of peaks less or equal to number of<br />
sinusoids).<br />
• The SOS approach builds <strong>on</strong> the assumpti<strong>on</strong> that any <strong>channel</strong> resp<strong>on</strong>se can be separated into a<br />
sum of reflectors represented as Dirac-functi<strong>on</strong>s in time <strong>and</strong> space.<br />
4.3.3 Problem details<br />
Not all of the above points might be obvious to the reader. In the following, some of the advantages <strong>and</strong><br />
disadvantages are thus explained in more details.<br />
4.3.3.1 Stochastic approach<br />
Filtering process. Essentially, the <strong>channel</strong> resp<strong>on</strong>se is correlated across space <strong>on</strong>ly. This correlati<strong>on</strong> is<br />
characterized by the spatial ACF, which is calculated by numerical integrati<strong>on</strong> from the APS. This spatial<br />
ACF is then mapped into two dimensi<strong>on</strong>s; the correlati<strong>on</strong> between signals at antenna elements depending<br />
<strong>on</strong> inter-element spacing, <strong>and</strong> the correlati<strong>on</strong> between signals in time depending <strong>on</strong> the movement of the<br />
mobile. This yields a two-dimensi<strong>on</strong>al kernel which is then used for filtering uncorrelated Gaussian<br />
samples.<br />
N<strong>on</strong>-stati<strong>on</strong>ary effects. The model is stati<strong>on</strong>ary by default. While the incorporati<strong>on</strong> of certain timevariable<br />
parameters is straightforward, e.g. Ricean K-factor, other n<strong>on</strong>-stati<strong>on</strong>ary effects, i.e. timeevoluti<strong>on</strong><br />
of Doppler spectrum or angle parameters, requires c<strong>on</strong>tinuous re-calculati<strong>on</strong> of the filter kernel<br />
<strong>and</strong> is thus computati<strong>on</strong>ally expensive.<br />
4.3.3.2 SOS approach<br />
Number of sinusoids required. In the SOS framework, fading is ensured by defining the positi<strong>on</strong> of the<br />
sinusoids in delay <strong>and</strong> angle in such a way that a minimum number of sinusoids always falls within the<br />
resoluti<strong>on</strong> capabilities of the observati<strong>on</strong> <strong>system</strong>. This minimum number between 4 <strong>and</strong> 8 [Gald04]<br />
ensures the observati<strong>on</strong> of a close to Rayleigh distributi<strong>on</strong>. If the number of sinusoids drops below this<br />
minimum amount, the observer will first see unusual distributi<strong>on</strong>s <strong>and</strong> finally identify single, discrete<br />
scatterers, both of which is typically not a desired effect. Note that str<strong>on</strong>g discrete scatterers, typically<br />
associated with LOS scenarios, are implemented as an opti<strong>on</strong>al additi<strong>on</strong>al comp<strong>on</strong>ent (SCM secti<strong>on</strong> "Line<br />
of sight") because the power of a single sinusoid is by definiti<strong>on</strong> fixed <strong>and</strong> small (e.g. 1/20 of a tap in<br />
SCM).<br />
In the following, we illustrate this point with an example. In the SOS framework, an APS is generated by<br />
changing the spacing of the sinusoids (because each sinusoid is defined to have equal power). A typical<br />
APS <str<strong>on</strong>g>report</str<strong>on</strong>g>ed for outdoor scenarios is a Laplacian functi<strong>on</strong> with a log-normal distributed AS [AIP02].<br />
Next, we determine how to distribute the sinusoids. The lowest density (sinusoids per degree) occurs at<br />
the outer ends of the Laplacian functi<strong>on</strong> <strong>and</strong> for large AS values (we pick 30 degree according to the<br />
reference). Assuming we want to be accurate to -20dB from the peak of the APS, then the angle range is<br />
roughly ±100 degrees from the centre. Let's say the maximum resoluti<strong>on</strong> of our observer is 10 degree (for<br />
example by using a highly directive antenna). In these 10 degrees we want to have a minimum of say 5<br />
sinusoids, i.e. a density of 5/10. The total required number of sinusoids then can be derived as 2345 per<br />
delay-tap.<br />
There are two ways to decrease this high number of sinusoids. One is the introducti<strong>on</strong> of variability in<br />
power of the sinusoids. The distributi<strong>on</strong> of taps can then be different to the APS <strong>and</strong>, with respect to the<br />
previous example, might be more uniform than Laplacian. Hence, in the limiting case of uniform<br />
distributi<strong>on</strong> of taps, the power at each sinusoid would vary according to the Laplace functi<strong>on</strong>, <strong>and</strong> the<br />
resulting number of sinusoids then would be 101.<br />
The sec<strong>on</strong>d approach for reducing the number of sinusoids is to assume that the AS at each path is not<br />
equal to the total AS (over all paths) but smaller (like in SCM). Following again the example from above<br />
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<strong>and</strong> choosing an AS of 3 degrees, the required number of sinusoids is 239 for equal amplitude sinusoids<br />
<strong>and</strong> 11 for sinusoids with varying amplitude.<br />
Peaky Doppler spectrum. Due to the fixed velocity, each scatterer can be attributed a single Doppler<br />
frequency comp<strong>on</strong>ent. The resulting Doppler spectrum is simply the additi<strong>on</strong> of these comp<strong>on</strong>ents, which<br />
are limited in number by the amount of sinusoids per path. Ways to mitigate this are to use a high number<br />
of sinusoids, to shorten the time-durati<strong>on</strong> of the drops (frequency resoluti<strong>on</strong> decreases), or introduce<br />
instati<strong>on</strong>arity of the velocity.<br />
Discrete Scatterer Framework. The assumpti<strong>on</strong> that any <strong>channel</strong> resp<strong>on</strong>se can be separated into a sum<br />
of reflectors represented as Dirac-functi<strong>on</strong>s in time <strong>and</strong> space implies infinite accuracy. The measurement<br />
<strong>system</strong> to obtain these parameters would need infinite b<strong>and</strong>width, antennas, <strong>and</strong> power. The<br />
parameterizati<strong>on</strong> is thus problematic from an exact physical interpretati<strong>on</strong> point of view.<br />
4.3.4 Comparis<strong>on</strong><br />
It is important to note that both approaches c<strong>on</strong>verge to each other with increasing number of sinusoids<br />
for SOS <strong>and</strong> decreasing length of stati<strong>on</strong>ary segments for the stochastic approach. Hence, the essential<br />
questi<strong>on</strong> is which model is preferable for reas<strong>on</strong>able assumpti<strong>on</strong>s about the <strong>channel</strong> <strong>and</strong> implementati<strong>on</strong><br />
parameters. Channels that can not even be assumed short-term stati<strong>on</strong>ary will be more difficult to<br />
implement (with equal computati<strong>on</strong>al complexity) as a stochastic model than with SOS. On the other<br />
h<strong>and</strong>, <strong>channel</strong>s with large AS per tap will be more difficult to implement (with equal computati<strong>on</strong>al<br />
complexity) as SOS than with a stochastic model.<br />
The WINNER Channel Model follows the SOS approach. It is seen as flexible framework <strong>and</strong> it enables<br />
more easily advanced future modelling features like time evoluti<strong>on</strong> of <strong>channel</strong> model parameters.<br />
4.3.5 Kr<strong>on</strong>ecker correlati<strong>on</strong><br />
Many stochastic MIMO <strong>channel</strong> <strong>models</strong> apply what is called the Kr<strong>on</strong>ecker assumpti<strong>on</strong> for the antenna<br />
correlati<strong>on</strong> matrices. This assumpti<strong>on</strong> states that the correlati<strong>on</strong> matrix, obtained as C = E{ vec(H)<br />
vec(H) H }, can be written as a Kr<strong>on</strong>ecker product, i.e. C = C Rx ⊗ C Tx , where C Rx <strong>and</strong> C Tx are receive <strong>and</strong><br />
transmit correlati<strong>on</strong> matrices, respectively. The Kr<strong>on</strong>ecker property is useful in many ways; most<br />
importantly it significantly reduces the number of model parameters, <strong>and</strong> it greatly simplifies the<br />
analytical treatment (such as for capacity evaluati<strong>on</strong>). It implies that the joint transmit <strong>and</strong> receive APS<br />
functi<strong>on</strong> can be written as a product of two independent APS at transmitter <strong>and</strong> receiver.<br />
Other publicati<strong>on</strong>s [HOHB02] based <strong>on</strong> measurement results have made a point that this assumpti<strong>on</strong><br />
could not be verified empirically in all scenarios evaluated. In reacti<strong>on</strong> to that, researchers have tried to<br />
come up with new methods that represent a compromise between the abstracti<strong>on</strong> <strong>and</strong> simplificati<strong>on</strong> of the<br />
Kr<strong>on</strong>ecker assumpti<strong>on</strong> <strong>and</strong> the rather complex case with no assumpti<strong>on</strong>s at all. In [OHWW03], an<br />
approach is presented where the c<strong>on</strong>diti<strong>on</strong> of a separable APS is alleviated into the c<strong>on</strong>diti<strong>on</strong> of<br />
independent eigenbasis of receiver to the transmit weights, <strong>and</strong> vice versa.<br />
Our preliminary analysis shows that, while both arguments certainly have significance, it is in practice<br />
important to carefully examine the underlying basis that the correlati<strong>on</strong> matrix is computed <strong>on</strong>. We start<br />
with the most detailed model. In case of a wideb<strong>and</strong> <strong>system</strong>, the <strong>channel</strong> is represented as a tapped delay<br />
line, i.e. H(τ) = H 1 δ(τ - τ 1 ) + … + H n δ(τ - τ n ). Furthermore, each delay-tap matrix can be split into a<br />
sum of c<strong>on</strong>tributi<strong>on</strong>s from different angle clusters, i.e. H i = H i1 + … + H im . We can now argue that with<br />
sufficient splitting <strong>and</strong> thus subdivisi<strong>on</strong> of the delay-angle domain, we can always reach a point such that<br />
all the smallest parts H ij have a separable APS <strong>and</strong> thus a Kr<strong>on</strong>ecker correlati<strong>on</strong> matrix. Any <strong>system</strong> with<br />
a resoluti<strong>on</strong> capability less than that will observe <strong>on</strong>ly linear combinati<strong>on</strong>s of H ij which might well not<br />
hold up to the Kr<strong>on</strong>ecker assumpti<strong>on</strong>. Thus we can always define a <strong>channel</strong> model based <strong>on</strong> Kr<strong>on</strong>ecker<br />
correlated comp<strong>on</strong>ents, while a <strong>system</strong> employing this model might not observe such properties.<br />
In summary this means that we can build a <strong>channel</strong> model by defining a set of clusters (in delay-angle<br />
domain) al<strong>on</strong>g with their independent APS at transmitter <strong>and</strong> receiver.<br />
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5. Measurements <strong>and</strong> Literature Review<br />
Our <strong>models</strong> are based <strong>on</strong> 3 pillars, namely existing (spatial) <strong>channel</strong> <strong>models</strong>, new publicati<strong>on</strong>s regarding<br />
all kind of <strong>channel</strong> modelling aspects, <strong>and</strong> finally measurements c<strong>on</strong>ducted within the work of WINNER.<br />
5.1 Measurement <strong>system</strong>s<br />
5.1.1 Principle of <strong>channel</strong> sounding<br />
The operati<strong>on</strong> principle of a <strong>channel</strong> sounder is to transmit a known signal using <strong>on</strong>e antenna in <strong>on</strong>e place<br />
<strong>and</strong> to receive it using another antenna in another place. The operati<strong>on</strong> is thus very similar to that of a<br />
vector network analyzer. The key difference is that the transmitter <strong>and</strong> receiver are separate units. For this<br />
reas<strong>on</strong>, both the receiver <strong>and</strong> transmitter must be phase locked to accurate frequency st<strong>and</strong>ards (typically<br />
Rubidium clocks) in order to maintain phase coherence.<br />
In simplest form the <strong>channel</strong> can be sounded by generating a CW RF signal with a signal generator at the<br />
transmitter, <strong>and</strong> mixing it down at the receiver using another generator tuned to the same frequency. The<br />
voltage at the mixer output gives the narrowb<strong>and</strong> radio <strong>channel</strong> as a functi<strong>on</strong> of time. In practice<br />
amplifiers <strong>and</strong> filters are also needed in the <strong>system</strong>. The advantage of this type of <strong>channel</strong> sounder is that<br />
it can be easily built using st<strong>and</strong>ard laboratory equipment. However, the drawback is that the wideb<strong>and</strong><br />
<strong>channel</strong> properties can’t be measured. KTH used this type of <strong>channel</strong> sounder in their WINNER<br />
measurements.<br />
In order to solve the wideb<strong>and</strong> properties of the radio <strong>channel</strong>, also the sounding itself needs to be d<strong>on</strong>e<br />
using a wideb<strong>and</strong> signal. The wideb<strong>and</strong> signal can be generated either using direct signal spreading (used<br />
e.g. in PropSound <strong>and</strong> HUT sounders) or OFDM-type of transmissi<strong>on</strong> (used in RUSK sounder).<br />
Naturally, also the receiver needs to be wideb<strong>and</strong>. The radio <strong>channel</strong> is estimated either in time or<br />
frequency domain by cross-correlating the received signal with a replica of the original transmitted signal.<br />
Typically this is d<strong>on</strong>e numerically after sampling the wideb<strong>and</strong> signal at the output of a vector<br />
demodulator. The result is either the complex impulse resp<strong>on</strong>se or frequency transfer functi<strong>on</strong> of the<br />
<strong>channel</strong> (these two are c<strong>on</strong>nected by a Fourier transform). In additi<strong>on</strong>, the effects of the transmitter <strong>and</strong><br />
receiver need to be removed from the result using e.g. inverse filtering methods.<br />
MIMO <strong>channel</strong> sounding requires transmissi<strong>on</strong> <strong>and</strong> recepti<strong>on</strong> using multiple antennas. In all wideb<strong>and</strong><br />
sounder <strong>system</strong>s used in WINNER measurements this was achieved with a single transmitter – receiver<br />
pair through time multiplexing with synchr<strong>on</strong>ous antenna switches in the transmitter <strong>and</strong> receiver. Using<br />
electr<strong>on</strong>ic RF switches the switching can be made fast enough to capture essentially the same radio<br />
<strong>channel</strong> with all antennas even in mobile measurements. In KTH measurement <strong>system</strong> the MIMO<br />
measurement was d<strong>on</strong>e using four parallel transmitters tuned at slightly different frequencies <strong>and</strong> four<br />
parallel receivers each receiving all transmitted frequency t<strong>on</strong>es.<br />
5.1.2 Channel sounders employed<br />
Four different radio <strong>channel</strong> measurement <strong>system</strong>s were used in the measurement campaigns. The results<br />
obtained with the HUT sounder in campaigns outside WINNER are used as background data in the<br />
<strong>channel</strong> model. The measurement <strong>system</strong>s are listed in Table 5.1.<br />
Partner<br />
Table 5.1: Measurement <strong>system</strong>s used in <strong>channel</strong> measurements.<br />
Measurement<br />
<strong>system</strong> type<br />
Manufacturer<br />
Hyper<strong>link</strong><br />
EBIT Propsound Elektrobit http://www.propsim.com/<br />
NOK<br />
Propsound<br />
+ HUT antennas<br />
Elektrobit<br />
http://www.propsim.com/<br />
TUI RUSK Medav http://www.<strong>channel</strong>sounder.de/<br />
KTH KTH specific N/A N/A<br />
HUT HUT specific N/A http://www.tkk.fi/Units/Radio/research/<br />
rf_applicati<strong>on</strong>s_in_mobile_communicati<strong>on</strong>/radio_<strong>channel</strong>/<br />
radio_<strong>channel</strong>_sounder.htm<br />
The PropSound TM <strong>and</strong> RUSK <strong>channel</strong> sounders are commercial wideb<strong>and</strong> radio <strong>channel</strong> measurements<br />
<strong>system</strong>s, while the HUT wideb<strong>and</strong> <strong>channel</strong> sounder is mainly self-made. The narrowb<strong>and</strong> measurement<br />
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<strong>system</strong> used by KTH is self-made based <strong>on</strong> commercial laboratory equipment. All measurements <strong>system</strong>s<br />
are described in more detail below. For further informati<strong>on</strong> the reader is advised to c<strong>on</strong>sult the web pages<br />
under the <strong>link</strong>s given in Table 5.1.<br />
5.1.2.1 PropSound <strong>channel</strong> sounder<br />
Figure 5.1 presents a schematic diagram of PropSound <strong>channel</strong> sounder. The sounder <strong>system</strong> c<strong>on</strong>sists of<br />
separate transmitter <strong>and</strong> receiver parts, which both are c<strong>on</strong>trolled through a pers<strong>on</strong>al computer. Both TX<br />
<strong>and</strong> RX are able to c<strong>on</strong>trol RF switches, which are synchr<strong>on</strong>ized in order to make time-multiplexed<br />
MIMO measurements.<br />
Figure 5.1: Block diagram of PropSound multidimensi<strong>on</strong>al sounder.<br />
NOK <strong>and</strong> EBIT both have a PropSound <strong>channel</strong> sounder. However, the <strong>system</strong>s have somewhat different<br />
characteristics. The key features of the EBIT PropSound <strong>channel</strong> sounder are listed in Table 5.2, while the<br />
NOK PropSound features are listed in Table 5.3.<br />
Table 5.2: Key features of EBIT PropSound <strong>channel</strong> sounder.<br />
Feature Value Note<br />
Frequency range<br />
1.7 GHz – 2.7 GHz<br />
Depending <strong>on</strong> RF unit<br />
Max. number of transmitter <strong>channel</strong>s<br />
(Tx-antennas)<br />
Max. number of receiver <strong>channel</strong>s (Rxantennas)<br />
Maximum RF power in antenna input<br />
(after TX switch)<br />
Chip rate<br />
Sequence length (defines maximum<br />
excess delay)<br />
5.1 GHz – 5.9 GHz<br />
64 Limited by the switch<br />
64 Limited by the switch<br />
26 dBm<br />
Up to 100 Mchips/s<br />
31 – 4096<br />
Propagati<strong>on</strong> delay resoluti<strong>on</strong> 10 ... 10000 ns ( = 1 / chip rate) With ISIS resoluti<strong>on</strong><br />
significantly better<br />
Max. meas. data storage rate<br />
27 Mb/sec<br />
Table 5.3: Key features of NOK PropSound sounder.<br />
Feature Value Note<br />
Frequency range 1.8−2.1GHz, 2.1−2.5GHz, depending <strong>on</strong> RF unit<br />
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Max. number of transmitter <strong>channel</strong>s<br />
(Tx-antennas)<br />
Max. number of receiver <strong>channel</strong>s (Rxantennas)<br />
Maximum RF power in antenna input<br />
(after TX switch)<br />
Max. zero-to-zero b<strong>and</strong>width<br />
Chip rate<br />
Sequence length (defines maximum<br />
excess delay)<br />
Sampling frequency<br />
Propagati<strong>on</strong> delay resoluti<strong>on</strong><br />
5.15−5.35 GHz, 5.725−5.875 GHz<br />
64 limited by NOK switch<br />
64 limited by NOK switch<br />
+24 dBm +35dBm with external<br />
power amplifier<br />
200 MHz<br />
0.5 ... 100 MHz<br />
31 ... 4096 chips<br />
1 ... 2000 MHz<br />
10 ... 10000 ns ( = 1 / chip rate)<br />
RF sensitivity -87 dBm (@100 MHz<br />
b<strong>and</strong>width)<br />
Max. meas. data storage rate<br />
2 x 20 Mbytes/s<br />
5.1.2.2 Medav <strong>channel</strong> sounder<br />
Figure 5.2 shows the principal structure of the Medav RUSK Channel Sounder [Medav]. Furthermore,<br />
Table 5.4 summarizes the technical key features of the sounder setup, which were used during the MIMO<br />
measurements.<br />
Mobile<br />
Transmitter<br />
RF down c<strong>on</strong>verter<br />
Receiver<br />
Digital demodulator<br />
Display<br />
Arbitrary<br />
waveform<br />
generator<br />
Local<br />
Oscillator<br />
Mux<br />
Tx<br />
array<br />
Rx array Mux<br />
~<br />
Local<br />
Oscillator<br />
~<br />
A<br />
D<br />
8 bit<br />
640 MHz<br />
PC<br />
hard disc<br />
array<br />
Rubidium<br />
frequency<br />
reference<br />
Positi<strong>on</strong> (GPS, wheel sensors)<br />
Rubidium<br />
frequency<br />
reference<br />
Positi<strong>on</strong> (data telemetry)<br />
Figure 5.2: Block diagram of the RUSK Channel Sounder from Medav.<br />
Table 5.4: Key features of the Medav RUSK <strong>channel</strong> sounder.<br />
Feature Value Note<br />
Frequency range 1.2 ….2.x GHz; 5…6 GHz customized<br />
Max. number of transmitter <strong>channel</strong>s<br />
(Tx-antennas)<br />
Max. number of receiver <strong>channel</strong>s (Rxantennas)<br />
Maximum RF power in antenna input<br />
(after TX switch)<br />
Test signal<br />
Sequence length (defines maximum<br />
excess delay)<br />
Up to 2 16 <strong>channel</strong>s, e.g.,<br />
also depending <strong>on</strong><br />
16 transmit <strong>and</strong><br />
switches<br />
64 receive antennas also depending <strong>on</strong><br />
switches<br />
2 W also depending <strong>on</strong><br />
switches<br />
Multi Carrier Spread Spectrum<br />
Signal (MCSSS)<br />
256 – 8192 spectral lines depending <strong>on</strong> IR length<br />
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Sampling frequency 320 MHz at Tx <strong>and</strong> Rx<br />
Propagati<strong>on</strong> delay resoluti<strong>on</strong><br />
4.17 ns (1/b<strong>and</strong>width)<br />
Impulse resp<strong>on</strong>se length 0.8 µs – 25.6 µs adjustable to the<br />
envir<strong>on</strong>ment<br />
RF sensitivity<br />
Max. meas. data storage rate<br />
-90 dBm<br />
2x160 Mbytes/s<br />
The RUSK Channel Sounder uses an excitati<strong>on</strong> signal c<strong>on</strong>cept, which is known as the “periodic multisine<br />
signal”. This approach is well known from frequency domain <strong>system</strong> identificati<strong>on</strong> in measurement<br />
engineering. In communicati<strong>on</strong> engineering teRMS this signal may be called a multicarrier spread<br />
spectrum signal (MCSSS). Regarding the overall spectral shape, the main advantage of multicarrier<br />
spread spectrum signal (MCSSS) is its “brickwall-type” shape which allows c<strong>on</strong>centrating the signal<br />
energy exactly to the b<strong>and</strong> of interest. This can even be multiple b<strong>and</strong>s when spectral magnitudes are set<br />
to zero. One example applicati<strong>on</strong> is FDD sounding, which means that the sounder simultaneously excites<br />
both the up- <strong>and</strong> the down-<strong>link</strong> b<strong>and</strong>. Figure 5.3 presents the MCSSS in time (top row, left) <strong>and</strong> frequency<br />
domain (top row, right).<br />
Figure 5.3: Broadb<strong>and</strong> multicarrier spread spectrum signal (MCSSS) magnitude in the time <strong>and</strong><br />
frequency domain (top row) <strong>and</strong> estimated CIR <strong>and</strong> received signal spectrum (bottom row).<br />
In case of multipath transmissi<strong>on</strong>, the power spectrum of the received signal is shaped by frequency<br />
selective fading as shown for example in Figure 5.3 (bottom row, right). Furthermore the impulse<br />
resp<strong>on</strong>se (bottom row, left), which would result from inverse Fourier transform of frequency resp<strong>on</strong>se, is<br />
shown in the same figure.<br />
A MIMO <strong>channel</strong> sounder measures the <strong>channel</strong> resp<strong>on</strong>se matrix between all M Tx antennas at the transmit<br />
side <strong>and</strong> all M Rx antennas at the receiver side. This could be carried out by applying a parallel multiple<br />
<strong>channel</strong> transmitter <strong>and</strong> receiver. However, true parallel <strong>system</strong>s not <strong>on</strong>ly are extremely expensive. They<br />
are also inflexible (when c<strong>on</strong>sidering changing the number of antenna <strong>channel</strong>s) <strong>and</strong> susceptible to phase<br />
drift errors. Also parallel operati<strong>on</strong> of the transmitter <strong>channel</strong>s would cause specific problems since the<br />
M Tx transmitted signals have to be separated at the receiver. A much more suitable sounder architecture is<br />
based <strong>on</strong> switched antenna access. A switched antenna sounder c<strong>on</strong>tains <strong>on</strong>ly <strong>on</strong>e physical transmitter <strong>and</strong><br />
receiver <strong>channel</strong>. Only the antennas <strong>and</strong> the switching <strong>channel</strong>s are parallel. This reduces the sensitivity to<br />
<strong>channel</strong> imbalance.<br />
Figure 5.4 shows the switching time frame of a sequential MIMO sounder using antenna arrays at both<br />
sides of the <strong>link</strong>, which is applied in the RUSK MIMO Channel Sounder. Any rectangle block in the<br />
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figure represents <strong>on</strong>e period of the transmit/receive signal. Synchr<strong>on</strong>ous switching at the Rx <strong>and</strong> Tx is<br />
required in order to clearly assign the received signal periods to any input-output combinati<strong>on</strong> of the<br />
<strong>channel</strong> matrix. Timing <strong>and</strong> switching frame synchr<strong>on</strong>izati<strong>on</strong> is established during an initial<br />
synchr<strong>on</strong>izati<strong>on</strong> process prior to measurement data recording <strong>and</strong> must be maintained over the complete<br />
measurement time even in the case of remote operati<strong>on</strong> of Tx <strong>and</strong> Rx. This is accomplished by rubidium<br />
reference oscillators at both Rx <strong>and</strong> Tx.<br />
M Tx<br />
M Rx<br />
Tx<br />
Rx<br />
Tx switching sequence<br />
Rx switching sequence<br />
Tx<br />
<strong>channel</strong><br />
Rx<br />
1<br />
2<br />
3<br />
1<br />
2<br />
3<br />
4<br />
t p = T x Signal period = τ max = maximum path excess delay<br />
time<br />
t s = τ max 2 M Tx M Rx = total snapshot time durati<strong>on</strong><br />
Figure 5.4: MIMO sounder switching time frame.<br />
5.1.2.3 KTH measurement <strong>system</strong><br />
The KTH measurement <strong>system</strong> is based <strong>on</strong> four parallel transmitters tuned to slightly different<br />
frequencies. Figure 5.5 presents a block diagram of the transmitter.<br />
∑<br />
Reference<br />
Antenna<br />
TX module 1<br />
TX module 2<br />
splitter<br />
splitter<br />
TX antenna 1<br />
TX antenna 2<br />
Laptop PC<br />
TX module 3<br />
splitter<br />
TX antenna 3<br />
TX module 4<br />
splitter<br />
TX antenna 4<br />
Figure 5.5: Block diagram of the transmitter chains.<br />
Each base stati<strong>on</strong> c<strong>on</strong>tains four parallel receiver chains as shown in Figure 5.6. The combined RF signal<br />
from each vertical array is input to its corresp<strong>on</strong>ding RX module. The output signal of the RX module is<br />
fed to an analog-to-digital c<strong>on</strong>verter (ADC). The ADC collects 40000 samples from each signal chain per<br />
sec<strong>on</strong>d <strong>and</strong> writes the sampled data to the hard disk of a computer. These raw data samples are processed<br />
<strong>and</strong> analyzed later by software in an off-line fashi<strong>on</strong>.<br />
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RX 1<br />
RX module 1<br />
RX 2<br />
RX module 2<br />
RX 3<br />
ADC<br />
PC<br />
RX module 3<br />
RX 4<br />
RX module 4<br />
Figure 5.6: Block diagram of the receiver chains.<br />
The frequencies are first estimated per segments of several sec<strong>on</strong>ds following which <strong>channel</strong> estimati<strong>on</strong> is<br />
performed. Both the transmitter <strong>and</strong> both base-stati<strong>on</strong> are c<strong>on</strong>nected to GPS. The transmitter is <strong>on</strong>-off<br />
keyed in order to facilitate fine temporal synchr<strong>on</strong>izati<strong>on</strong> at the base-stati<strong>on</strong>s.<br />
5.1.2.4 HUT <strong>channel</strong> sounder<br />
The working principle of the HUT <strong>channel</strong> sounder is almost identical to the Elektrobit PropSound<br />
<strong>channel</strong> sounder. The TX unit is a st<strong>and</strong>al<strong>on</strong>e unit synchr<strong>on</strong>ized with the RX unit in order to synchr<strong>on</strong>ize<br />
the <strong>channel</strong> switching in both TX <strong>and</strong> RX end before performing time-multiplexed MIMO measurements.<br />
The RX unit is used at the mobile end <strong>and</strong> is c<strong>on</strong>trolled through a pers<strong>on</strong>al computer. The sounder has 2,<br />
5 <strong>and</strong> 60 GHz frequency ranges. The key features of the HUT 5 GHz <strong>channel</strong> sounder, whose results are<br />
used in WINNER, are presented in Table 5.5.<br />
Table 5.5: Key features of HUT 5 GHz <strong>channel</strong> sounder.<br />
Feature Value Note<br />
Frequency range<br />
5.25– 5.35 GHz<br />
Max. number of transmitter <strong>channel</strong>s<br />
(Tx-antennas)<br />
Max. number of receiver <strong>channel</strong>s (Rxantennas)<br />
Maximum RF power in antenna input<br />
(after TX switch)<br />
Chip rate<br />
Sequence length (defines maximum<br />
excess delay)<br />
Propagati<strong>on</strong> delay resoluti<strong>on</strong><br />
Max. meas. data storage rate<br />
32 Limited by the switch<br />
32 Limited by the switch<br />
4 W<br />
Up to 60 Mchips/s<br />
255<br />
17 ns ( = 1 / chip rate)<br />
2*20 Mb/sec<br />
5.2 Measurement campaigns<br />
Several measurement campaigns have been designed <strong>and</strong> performed in WINNER WP5 during 2004 to<br />
obtain parameters for the WINNER <strong>channel</strong> <strong>models</strong>. The following secti<strong>on</strong>s give overviews of the<br />
campaigns.<br />
5.2.1 Scenario A1<br />
5.2.1.1 EBIT campaign<br />
Measurements c<strong>on</strong>ducted during 2004 for A1 were performed at two centre-frequencies, 2.45 <strong>and</strong> 5.25<br />
GHz at Elektrobit premises in Oulu, Finl<strong>and</strong>. The measurement results were included in the deliverable<br />
D5.3 [D5.3].<br />
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In 2005 a new series of measurements was performed at two locati<strong>on</strong>s, Oulu University main building<br />
<strong>and</strong> Oulu University wing building Tietotalo. Two different buildings were measured at 5.25 GHz with<br />
100 MHz b<strong>and</strong>width. In the two buildings, more than 8 BSs were chosen with many different routes.<br />
Tietotalo is a typical office envir<strong>on</strong>ment, the corridors of which are narrow with widths around 1.8<br />
meters. In the university main building, the corridors have different width, the widest is around 3.5<br />
meters. In the room measurements at the university main building, the room size is very close to 10 m by<br />
10 m, as in the definiti<strong>on</strong> of the scenario A1. In Tietotalo the sizes of the measured rooms were<br />
comparable to 10 m by 10 m. The largest combined sets of IRs for deriving <strong>channel</strong> <strong>models</strong> <strong>and</strong><br />
parameters are over 55000.<br />
The indoor envir<strong>on</strong>ments here are divided into the following 4 cases:<br />
(1) Corridor-corridor LOS (c-c LOS): both BS <strong>and</strong> MS were placed at the corridors.<br />
(2) Room-corridor <strong>and</strong> corridor-room NLOS (r-c NLOS): BS in a room, MS in an adjacent corridor<br />
vice versa.<br />
(3) Room-room LOS/OLOS (r-r LOS/OLOS): both BS <strong>and</strong> MS in the rooms.<br />
(4) Corridor-corridor NLOS (c-c NLOS)<br />
In the analysis, 100 IRs for a drop are used (about 1.4 meters), if no window length is menti<strong>on</strong>ed.<br />
The following definiti<strong>on</strong>s are used: paths = peaks = ZDSC, Noise cut <strong>level</strong>s used in the analysis: For<br />
LOS: 28 ~ 35 dB. For NLOS: 15 dB ~ 30 dB.<br />
5.2.2 Scenario B1<br />
5.2.2.1 HUT background campaign<br />
These measurements were performed outside the WINNER project by HUT <strong>and</strong> they were used as<br />
background data for Scenario B1. The sounder was the HUT sounder with centre-frequency 5.3 GHz <strong>and</strong><br />
b<strong>and</strong>width (chip rate) 60 MHz. The measurements in the micro-cell envir<strong>on</strong>ment included both LOS <strong>and</strong><br />
NLOS routes in a fairly regular rectangle street grid with 4-5-storey buildings. In the measurements the<br />
fixed Tx antenna was a 16-element dual-polarised planar patch antenna array <strong>and</strong> the mobile Rx antenna a<br />
semispherical antenna with 15 dual-polarized patch elements. The antennas are shown in Figure 5.9.<br />
5.2.3 Scenario B3<br />
5.2.3.1 TUI campaign<br />
These measurements were d<strong>on</strong>e with the RUSK ATM MIMO sounder by Medav [Medav]. The centrefrequency<br />
in the measurements was 5.2 GHz <strong>and</strong> the b<strong>and</strong>width was 120 MHz. The indoor envir<strong>on</strong>ment<br />
c<strong>on</strong>sisted of a university lecture hall (c<strong>on</strong>ference hall) <strong>and</strong> the entrance area (foyer) next to it. The foyer is<br />
characterized by a 2 floor open envir<strong>on</strong>ment, with dimensi<strong>on</strong> of 15m x 30m x 8m. The c<strong>on</strong>ference hall is<br />
a typical lecture hall envir<strong>on</strong>ment with slowly elevated sitting rows; the dimensi<strong>on</strong> is 30m x 35m x 15m.<br />
In all measurement cases the Tx was moving <strong>on</strong> the same track, whereby the positi<strong>on</strong> of the Rx was<br />
changed in the hall. Within the c<strong>on</strong>ference hall scenario two different main setups where used, namely<br />
LOS <strong>and</strong> NLOS/OLOS, where in the latter the LOS was obstructed by an absorber mat.<br />
The pictures of high-resoluti<strong>on</strong> antennas used in TUI measurement campaign are shown in Figure 5.7.<br />
Figure 5.7: High-resoluti<strong>on</strong> antennas used in TUI campaigns. Left: 16-element uniform circular<br />
array, right: dual-polarized 8-element uniform linear array.<br />
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5.2.4 Scenario C1<br />
5.2.4.1 EBIT campaign<br />
Measurements were c<strong>on</strong>ducted during 2004 for the suburban scenario C1 at the centre-frequency 5.25<br />
GHz. Measurements by Elektrobit were performed in Heinäpää relatively near to Oulu centre in an area,<br />
where the houses are lower than in the centre of the town, with some parking lots, parks <strong>and</strong> trees al<strong>on</strong>g<br />
the streets in between the houses. The height of the houses varied typically from 3 to 6 stories. Due to the<br />
measurement routes mainly LOS c<strong>on</strong>diti<strong>on</strong>s were encountered. It should be noted that this envir<strong>on</strong>ment<br />
was clearly different from the envir<strong>on</strong>ment, where Nokia performed their measurements for C1 scenario.<br />
5.2.4.2 NOK <strong>and</strong> HUT campaign<br />
Measurements were d<strong>on</strong>e using the NRC/RAD/EDM radio <strong>channel</strong> sounder (Elektrobit PropSound). The<br />
centre-frequency used in the measurements was 5.3 GHz, <strong>and</strong> the chip rate was 100 MHz. The<br />
measurement campaign was targeted to support the following WINNER <strong>channel</strong> model development<br />
purposes:<br />
• Creati<strong>on</strong> of a path-loss model<br />
• MIMO performance evaluati<strong>on</strong><br />
• Doppler spectra characterizati<strong>on</strong><br />
• Directi<strong>on</strong>-of-departure (DoD) <strong>and</strong> directi<strong>on</strong>-of-arrival (DoA) analysis<br />
Altogether three base stati<strong>on</strong> sites (some of them with two sectors) were measured in two different types<br />
of suburban envir<strong>on</strong>ments in Helsinki, Finl<strong>and</strong>. The first envir<strong>on</strong>ment was a residential area occupied<br />
mostly with low, <strong>on</strong>e or two floor single family or terraced houses with occasi<strong>on</strong>al open areas, such as<br />
playgrounds or gardens, in between. The sec<strong>on</strong>d envir<strong>on</strong>ment was rather loosely built residential area<br />
with higher 3-4 floor high apartment buildings. Open areas, play grounds <strong>and</strong> small forest secti<strong>on</strong>s were<br />
in between the apartment buildings, as well as occasi<strong>on</strong>al lower 1-2 floor buildings.<br />
A truck-operated crane was used to lift up the transmitter. The receiving antenna (user mockup) was<br />
located <strong>on</strong> top of the van, <strong>and</strong> the receiver sounder unit was inside the van. In this case several c<strong>on</strong>tinuous<br />
routes of lengths 50…800 meters each were measured. For DoA/DoD measurements the spherical array<br />
<strong>and</strong> the sounder receiver unit were set to a battery-powered trolley to allow slow enough moving speeds.<br />
Due to huge amounts of data in this case shorter routes of 10-20 meters were measured in dozens of<br />
different locati<strong>on</strong>s within the sector. Some antennas used in Nokia <strong>and</strong> HUT campaign are shown in<br />
Figure 5.8.<br />
Figure 5.8: Left: spherical antenna array (HUT Radio Laboratory) used in the mobile end of the<br />
radio <strong>link</strong> fro DOA characterizati<strong>on</strong>. Right: Planar dual-polarized array (HUT Radio Laboratory)<br />
used as a base stati<strong>on</strong> antenna.<br />
5.2.5 Scenario C2<br />
5.2.5.1 KTH measurements<br />
A measurement campaign was c<strong>on</strong>ducted in the Vasastan area of Stockholm city. The area can be<br />
characterized as a typical European urban with mostly six to eight stories high st<strong>on</strong>e buildings <strong>and</strong><br />
occasi<strong>on</strong>al higher buildings <strong>and</strong> church towers. The measurements were d<strong>on</strong>e in up<strong>link</strong> directi<strong>on</strong> with a<br />
mobile-stati<strong>on</strong> transmitting four separate CWs <strong>on</strong> four separate antennas with a frequency separati<strong>on</strong> of<br />
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approximately <strong>on</strong>e kilohertz <strong>and</strong> a nominal centre-frequency of 1766.6MHz. The four MS antennas are<br />
slanted patch with a half-power beam-width of 80-degree, which point in four different directi<strong>on</strong>s offset<br />
90-degrees from each other, see Figure 5.9. The CW of the transmit antennas are not frequency locked,<br />
resulting in an unknown phase-offset in the four vector <strong>channel</strong>s between the mobile-stati<strong>on</strong> <strong>and</strong> <strong>on</strong>e<br />
base-stati<strong>on</strong> will result. The signals transmitted by the four antenna at MS are received by two basestati<strong>on</strong>s,<br />
down-c<strong>on</strong>verted to complex I&Q base-b<strong>and</strong> <strong>and</strong> saved <strong>on</strong> a hard-disc. One of the base stati<strong>on</strong>s<br />
(Kårhuset-A) has a four-element antenna array <strong>and</strong> is placed <strong>on</strong> a roof barely above the average building<br />
height in its sector of coverage. The other base-stati<strong>on</strong> has two four antenna arrays c<strong>on</strong>nected (Vanadis-B<br />
<strong>and</strong> Vanadis-C) <strong>and</strong> is placed <strong>on</strong> a roof some ten meters above the average building height. Views from<br />
basestati<strong>on</strong> are shown in Figure 5.10. The two arrays are located <strong>on</strong> different edges of the same building<br />
some 20-meters from each other <strong>and</strong> offset 120-degrees in pointing directi<strong>on</strong>. In fr<strong>on</strong>t of Vanadis-B are<br />
some trees, which may have an impact <strong>on</strong> the propagati<strong>on</strong>. The distance between the two sites is 900<br />
meters <strong>and</strong> the measurements were d<strong>on</strong>e in with mobile located in the area between the two BSs. The<br />
path-loss slope from Kårhuset was estimated to be around 40-45dB/dec while it is 25-30dB/dec from<br />
Vanadis. The total measurement route covers about 15km of mobile trajectory.<br />
Figure 5.9: Left: MS antennas, right: BS antenna array.<br />
Figure 5.10: Left: View from “Kårhuset-A” basestati<strong>on</strong>. Right: View from “Vanadis-B” BS.<br />
5.2.6 Scenario D1<br />
5.2.6.1 EBIT campaign<br />
Measurements c<strong>on</strong>ducted during 2004 for D1 were performed at two centre-frequencies, 2.45 <strong>and</strong> 5.25<br />
GHz in Tyrnävä, a small village near Oulu, <strong>and</strong> its surroundings. The measurement results were included<br />
in the deliverable D5.3 [D5.3].<br />
In the year 2005 three new BS locati<strong>on</strong>s were measured at 5.25 GHz with 100 MHz b<strong>and</strong>width. In<br />
additi<strong>on</strong>, PL measurements were c<strong>on</strong>ducted with a smaller b<strong>and</strong>-width 10 MHz to increase the sensitivity<br />
of the receiver equipment. This arrangement allowed us to obtain path losses up to 10 km distance.<br />
At the same locati<strong>on</strong>s some measurements at 2.45 GHz were performed to investigate the effect of centrefrequency<br />
<strong>on</strong> the path loss <strong>and</strong> other <strong>channel</strong> parameters. For practical reas<strong>on</strong>s the measurements at 2.45<br />
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GHz were c<strong>on</strong>ducted in a smaller scope with fewer routes. However, the routes used were the same as the<br />
routes used for the 5.25 GHz measurements.<br />
Map of the measurement envir<strong>on</strong>ment <strong>and</strong> the BS locati<strong>on</strong>s are shown in Figure 5.11.<br />
Figure 5.11 Base stati<strong>on</strong> locati<strong>on</strong>s for the Tyrnävä measurements in the summer 2005.<br />
5.2.7 Measurement summary<br />
Table 5.6 presents a summary of WINNER measurement campaigns <strong>and</strong> shows which scenarios are<br />
supported by measurement data.<br />
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Table 5.6: Summary of WINNER <strong>channel</strong> measurements.<br />
Corresp<strong>on</strong>ding test<br />
scenario<br />
Partner Measurement type Locati<strong>on</strong> Envir<strong>on</strong>ment descripti<strong>on</strong> Center<br />
frequency<br />
A1 (Indoor) EBIT Indoor (office<br />
building)<br />
Oulu, Finl<strong>and</strong> Typical office, cubicles, corridors 5.25 GHz 100 MHz (chip<br />
rate)<br />
B<strong>and</strong>width TX power BTS<br />
height<br />
MS<br />
height<br />
MS speed Max MS-BS<br />
distances<br />
# of BTS<br />
antennas<br />
# of MS<br />
antennas<br />
BTS antenna type MS antenna<br />
type<br />
23 dBm 2.1 m 0.9 m ~1 m/s < 100 m 32, 1 or 16 52, 1 or 18 Dual-polarized 4x4<br />
UPA, dipole<br />
Dual-polarized<br />
2x9 ODA, dipole<br />
A1 (Indoor) EBIT Indoor (office<br />
building)<br />
Oulu, Finl<strong>and</strong> Typical office, cubicles, corridors 2.45 GHz 100 MHz (chip<br />
rate)<br />
23 dBm 2.1 m 0.9 m ~1 m/s < 100 m 32, 1 or 16 56, 1 or 18 Dual-polarized 4x4<br />
UPA, dipole<br />
Dual-polarized<br />
3x8 ODA, dipole<br />
B1 LOS (urban) HUT Microcell LOS Helsinki,<br />
Finl<strong>and</strong><br />
B1 NLOS (urban) HUT Microcell NLOS Helsinki,<br />
Finl<strong>and</strong><br />
B1 / D1<br />
(suburban/rural)<br />
Microcellular city center<br />
measurements with LOS<br />
Microcellular city center<br />
measurements with NLOS<br />
TUI Outdoor, Hot spot Ulm, Germany suburban/rural area with highway<br />
bridge, Hot spot (public access),<br />
B3 (Indoor) TUI Indoor (large<br />
lecture hall <strong>and</strong> )<br />
Ilmenau,<br />
Germany<br />
Car to Bridge<br />
5.3 GHz 60 MHz (chip<br />
rate)<br />
5.3 GHz 60 MHz (chip<br />
rate)<br />
36 dBm at<br />
antenna<br />
input<br />
36 dBm at<br />
antenna<br />
input<br />
5.2 GHz 120 MHz 27 dBm at<br />
TX switch<br />
Auditorium of TUI, large indoor hall 5.2 GHz 120 MHz 27 dBm at<br />
TX switch<br />
10 m 1.6 m 0.2 m/s 400 m 16 dualpolarized<br />
10 m 1.6 m 0.2 m/s 180 m 16 dualpolarized<br />
15 dual-polarized<br />
=> <strong>channel</strong>s<br />
32x30<br />
15 dual-polarized<br />
=> <strong>channel</strong>s<br />
32x30<br />
4x4 planar array<br />
with +/-45<br />
polarizati<strong>on</strong>s<br />
4x4 planar array<br />
with +/-45<br />
polarizati<strong>on</strong>s<br />
~5 m 2.10 m up to ~ 10 m/s 100-150m 8x2 16 ULA of 8 dualpolarized<br />
(V/H)<br />
patch elements<br />
3.95 m <strong>and</strong><br />
(3 m +<br />
3.65 m)<br />
1.10 m fixed positi<strong>on</strong>s,<br />
~1m/s UCA16<br />
at a track<br />
30m 8x2 16 ULA of 8 dualpolarized<br />
(V/H)<br />
patch elements<br />
spherical with<br />
H/V polarizati<strong>on</strong>s<br />
spherical with<br />
H/V polarizati<strong>on</strong>s<br />
UCA of 16,<br />
vertical polar.<br />
UCA of 16,<br />
vertical polar.<br />
C1 metropol<br />
(suburban)<br />
C1 metropol<br />
(suburban)<br />
EBIT Suburban macro Oulu, Finl<strong>and</strong> Suburban residential: max. 2-<br />
storey houses, BTS antenna above<br />
rooftop <strong>level</strong>, outdoors LOS<br />
obstructed mainly <strong>on</strong>ly by<br />
NOK &<br />
HUT<br />
Suburban macro Helsinki,<br />
Finl<strong>and</strong><br />
(Paloheinä,<br />
Munkkivuori)<br />
C2 metropol (urban) KTH Typical (European)<br />
Urban.<br />
vegetati<strong>on</strong><br />
Suburban residential: max. 2-store<br />
houses, BTS antenna above<br />
rooftop <strong>level</strong>, outdoors LOS<br />
obstructed mainly <strong>on</strong>ly by<br />
vegetati<strong>on</strong><br />
Stockholm City Urban macrocell. DCS1800<br />
up<strong>link</strong><br />
5.25 GHz 100 MHz (chip<br />
rate)<br />
5.3 GHz 100 MHz (chip<br />
rate)<br />
23 dBm 11.7m / 7.6<br />
m<br />
39 dBm (at<br />
antenna<br />
input)<br />
CW 18 dBm /<br />
antenna<br />
~25 m 2 m (<strong>on</strong><br />
top of a<br />
van)<br />
some 10 m<br />
above<br />
rooftop &<br />
just above<br />
rooftop<br />
1.8 m ~3 m/s 1 km 32, 1 or 16 52, 1 or 18 Dual-polarized 4x4<br />
UPA, dipole<br />
~1 m/s<br />
(pedestrian,<br />
with trolley);<br />
~10 m/s<br />
(vehicular, with<br />
car)<br />
< 1.1-1.3 km,<br />
depending <strong>on</strong><br />
the setup<br />
16 elements<br />
(each 2<br />
polarizati<strong>on</strong>s)<br />
1.8m 0-15m/s 1.2km 4/array (3<br />
arrays)<br />
4 (terminal mock<br />
up) or 14<br />
(spherical array)<br />
=> <strong>channel</strong>s: 8x4,<br />
4x2 (for MIMO,<br />
PL, Doppler),<br />
32x28 (for<br />
DoD/DoD)<br />
Dual-polarized (+/-<br />
45) planar array<br />
4 ULA with -45pol.<br />
slanted 4-stacked<br />
patch elements.<br />
0.55lambda<br />
spacing.<br />
Dual-polarized<br />
2x9 ODA, dipole<br />
terminal mock-up<br />
or spherical array<br />
(with trolley for<br />
DoA/DoD)<br />
Four -45polarized<br />
patches <strong>on</strong> the<br />
sides of a cube.<br />
C4 metropol (urban)<br />
outdoor-to-indoor<br />
KTH Typical (European)<br />
Urban.<br />
Stockholm City Urban macrocell. DCS1800<br />
up<strong>link</strong><br />
CW 18 dBm /<br />
antenna<br />
some 10 m<br />
above<br />
rooftop &<br />
just above<br />
rooftop<br />
1.8m ˜ 0.9m/s 4/array (3<br />
arrays)<br />
4 ULA with -45pol.<br />
slanted 4-stacked<br />
patch elements.<br />
0.55lambda<br />
spacing.<br />
Four -45polarized<br />
patches <strong>on</strong> the<br />
sides of a cube.<br />
D1 (rural) EBIT Wide area rural Tyrnävä,<br />
Finl<strong>and</strong><br />
Countryside, very flat, BTS<br />
antennas at mast, mainly LOS<br />
5.25 GHz 100 MHz (chip<br />
rate)<br />
23 dBm 17.6 m 1.7 m ~3 m/s 1.7 km (at<br />
maximum)<br />
32, 1 or 16 52, 1 or 18 Dual-polarized 4x4<br />
UPA, dipole<br />
Dual-polarized<br />
2x9 ODA, dipole<br />
D1 (rural) EBIT Wide area rural Tyrnävä,<br />
Finl<strong>and</strong><br />
Countryside, very flat, BTS<br />
antennas at mast, mainly LOS<br />
2.45 GHz 100 MHz (chip<br />
rate)<br />
23 dBm 17.6 m 1.7 m ~3 m/s 1.7 km (at<br />
maximum)<br />
32, 1 or 16 56, 1 or 18 Dual-polarized 4x4<br />
UPA, dipole<br />
Dual-polarized<br />
3x8 ODA, dipole<br />
NA NOK &<br />
HUT<br />
Urban ad hoc Helsinki,<br />
Finl<strong>and</strong><br />
(Ruoholahti)<br />
Urban outdoor, 5-6 store houses,<br />
indoor: metro stati<strong>on</strong>, supermarket<br />
NA HUT Indoor ad hoc Espoo, Finl<strong>and</strong> University office buildings, both<br />
older <strong>and</strong> modern<br />
Explanati<strong>on</strong>s:<br />
ULA = Uniform linear array<br />
UPA = Uniform planar array<br />
ODA = Omni directi<strong>on</strong>al array<br />
5.3 GHz 100 MHz (chip<br />
rate)<br />
5.3 GHz 60 MHz (chip<br />
rate)<br />
39 dBm (at<br />
antenna<br />
input)<br />
36 dBm at<br />
antenna<br />
input<br />
1.5 m 1.5 m ~0.83 m/s < 100 m 15 elements<br />
(each 2<br />
polarizati<strong>on</strong>s)<br />
1.6 m 1.6 m 0.2 m/s <br />
15 dual-polarized<br />
=> <strong>channel</strong>s<br />
30x30<br />
Spherical with +/-<br />
45 polarizati<strong>on</strong>s<br />
spherical with +/-45<br />
polarizati<strong>on</strong>s<br />
Spherical with<br />
H/V polarizati<strong>on</strong>s<br />
spherical with<br />
H/V polarizati<strong>on</strong>s<br />
Other informati<strong>on</strong><br />
Separate measurement setups<br />
for pathloss, MIMO<br />
characterizati<strong>on</strong>, Doppler <strong>and</strong><br />
DoA/DoD.<br />
Separate measurement setups<br />
for pathloss, MIMO<br />
characterizati<strong>on</strong>, Doppler <strong>and</strong><br />
DoA/DoD.<br />
Background data of HUT<br />
Background data of HUT<br />
MIMO meas. for DoA <strong>and</strong> DoD<br />
MIMO meas. for DoA <strong>and</strong> DoD<br />
Separate measurement setups<br />
for pathloss, MIMO<br />
characterizati<strong>on</strong>, Doppler <strong>and</strong><br />
DoA/DoD.<br />
Separate measurement setups<br />
for pathloss, MIMO<br />
characterizati<strong>on</strong>, Doppler <strong>and</strong><br />
DoA/DoD. Planar-to-spherical<br />
array measurements with trolley<br />
<strong>on</strong>ly for DoA/DoD.<br />
Signal received from three<br />
sectors with 4-element arrays<br />
distributed <strong>on</strong> two base-stati<strong>on</strong><br />
sites. Four closely spaced CW<br />
frequencies.<br />
Signal received from three<br />
sectors with 4-element arrays<br />
distributed <strong>on</strong> two base-stati<strong>on</strong><br />
sites. Four closely spaced CW<br />
frequencies.<br />
Separate measurement setups<br />
for pathloss, MIMO<br />
characterizati<strong>on</strong>, Doppler <strong>and</strong><br />
DoA/DoD.<br />
Separate measurement setups<br />
for pathloss, MIMO<br />
characterizati<strong>on</strong>, Doppler <strong>and</strong><br />
DoA/DoD.<br />
Mobile peer-to-peer type of<br />
measurement, mainly for<br />
DoA/DoD<br />
Mobile peer-to-peer type of<br />
measurement<br />
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5.3 Descripti<strong>on</strong> of key references<br />
5.4 Results of analysis items<br />
A list of analysis items was made, see [WP5AI]. The analysis items were divided into to categories<br />
namely priority 1 <strong>and</strong> priority 2. In the following two subsecti<strong>on</strong>s, selected set of analysis results from the<br />
campaigns are listed. Additi<strong>on</strong>al results are found in the appendices <strong>and</strong> in the measurement <str<strong>on</strong>g>report</str<strong>on</strong>g>s<br />
[WP5AR].<br />
The measurements data that has been used for extracti<strong>on</strong> of <strong>channel</strong> parameters for scenario B1 are not<br />
WINNER measurement data but can be c<strong>on</strong>sidered as background data from Helsinki University of<br />
Technology, Finl<strong>and</strong>. Sounder frequency is 5.3 GHz with 60 MHz chip rate <strong>and</strong> 120 MHz sampling rate<br />
for each I <strong>and</strong> Q comp<strong>on</strong>ent of the signal. A cutting threshold of 20 dB from the peaks of PDPs is adopted<br />
in the measurement results shown below. However, when there are good propagati<strong>on</strong> c<strong>on</strong>diti<strong>on</strong>s like LOS<br />
cases, a higher threshold is used. It is said that propagati<strong>on</strong> is LOS if an unobstructed path between the<br />
locati<strong>on</strong> of the transmitter <strong>and</strong> the locati<strong>on</strong> of the receiver exists.<br />
5.4.1 Path-loss <strong>and</strong> shadow fading<br />
Path-loss (PL) <strong>and</strong> shadow fading (SF) are c<strong>on</strong>sidered to be parameters of the highest priority in <strong>channel</strong><br />
modeling. Path-loss is loss of signal power between transmitter <strong>and</strong> receiver end. SF is the variance of the<br />
PL.<br />
The noise threshold was selected to be -20 dB from the impulse resp<strong>on</strong>se (IR) peak, <strong>and</strong> the IR samples<br />
below that are removed. Data within a small area is averaged in order to remove the effect of fast fading.<br />
The averaging window depended <strong>on</strong> the envir<strong>on</strong>ment <strong>and</strong> the centre-frequency. Spatial averaging is d<strong>on</strong>e<br />
by combining the wideb<strong>and</strong> MIMO matrices in power.<br />
PL at a certain snapshot is calculated from the calibrated IRs as a wideb<strong>and</strong> path loss<br />
PL = −10log<br />
( ∑ h(<br />
)<br />
10<br />
τ i<br />
2<br />
τ<br />
i<br />
) + GT<br />
+ GR<br />
(5.1)<br />
where G T <strong>and</strong> G R are antenna gains at the transmitter <strong>and</strong> at the receiver, respectively.<br />
The path-loss model is derived using linear regressi<strong>on</strong> (LMSE) of the scatter plot of the PL vs. distance<br />
between the transmitter <strong>and</strong> the receiver: PL(d) = A log 10 (d) + B = 10 n log 10 (d) + B, where B is PL<br />
intercept, <strong>and</strong> n is the PL exp<strong>on</strong>ent.<br />
5.4.1.1 Scenario A1<br />
5.4.1.1.1 Measurements in corridor - corridor LOS c<strong>on</strong>diti<strong>on</strong>s<br />
The measurements were c<strong>on</strong>ducted in corridors, the width of which was ranging from 1.5 to 3.5, <strong>and</strong> so<br />
that the BS <strong>and</strong> MS were in the same corridor with a LOS between them. The path-loss curve for the 5.25<br />
GHz centre-frequency in corridor-corridor LOS propagati<strong>on</strong> c<strong>on</strong>diti<strong>on</strong> can be seen in the Figure 5.12.<br />
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Figure 5.12. Indoor path loss in corridor – corridor LOS c<strong>on</strong>diti<strong>on</strong>s.<br />
The equati<strong>on</strong> for the path loss can now be expressed as<br />
PL(d) = 46.8 + 18.7 log 10 (d), s = 3.1 dB ( 5.2)<br />
where d is the distance <strong>and</strong> s is the st<strong>and</strong>ard deviati<strong>on</strong> of the shadow fading. The equati<strong>on</strong> is valid from 1<br />
m to 200 m.<br />
Very similar results were obtained in the previous measurement campaign [D5.3] for LOS c<strong>on</strong>diti<strong>on</strong>s, but<br />
for limited range. Other similar results, with the path-loss exp<strong>on</strong>ent less than 2, have been discussed in<br />
several references cited in Secti<strong>on</strong> 5.5.<br />
Figure 5.13. Shadow fading distributi<strong>on</strong> in an indoor corridor – corridor LOS envir<strong>on</strong>ment.<br />
5.4.1.1.2 Measurements in room - corridor NLOS c<strong>on</strong>diti<strong>on</strong>s<br />
The path-loss curve for the 5.25 GHz centre-frequency in room – corridor (corridor – room) LOS<br />
propagati<strong>on</strong> c<strong>on</strong>diti<strong>on</strong>s can be seen in Figure 5.14. The measurements were performed so that either the<br />
BS was in the corridor <strong>and</strong> the MS in the room al<strong>on</strong>g the corridor or vice versa. The wooden doors from<br />
the corridor to the rooms were closed. It was measured that the attenuati<strong>on</strong> of the door was 4 dB.<br />
Figure 5.14. Indoor path loss in room – corridor LOS c<strong>on</strong>diti<strong>on</strong>s.<br />
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The equati<strong>on</strong> for the path loss can now be expressed as:<br />
PL (d) = 38.8 + 36.8 log 10 (d), s = 3.5 dB (5.3)<br />
where d is the distance <strong>and</strong> s is the st<strong>and</strong>ard deviati<strong>on</strong> of the shadow fading.<br />
The equati<strong>on</strong> is valid from 3 m to 50 m. It is assumed that it can be used until 100 m, although this has<br />
not been verified by measurements. From 1 m to 3 m free-space loss formula should be used.<br />
Quite natural assumpti<strong>on</strong> is that most part of the path between the MS <strong>and</strong> BS the signal propagates in the<br />
corridor. Therefore it is slightly surprising that the path loss is much steeper in this case than in the<br />
corridor – corridor propagati<strong>on</strong>. The reas<strong>on</strong> must be in the mechanism by which the wave couples from<br />
the corridor to the room, or vice versa.<br />
5.4.1.2 Scenario B1<br />
The received power is calculated by summing over all antennas <strong>and</strong> delay bins in power in each<br />
measurement point. The calibrati<strong>on</strong> is d<strong>on</strong>e based <strong>on</strong> back to back measurement in anechoic chamber.<br />
Antenna gains are not included in the received power. The fitting of the parameters is d<strong>on</strong>e using least<br />
square error method. Path-loss model for LOS propagati<strong>on</strong> c<strong>on</strong>diti<strong>on</strong> is presented in Figure 5.15 with<br />
measurements. Equati<strong>on</strong> (5.4) is the path-loss model for LOS case <strong>and</strong> Equati<strong>on</strong> (5.5) is for NLOS case.<br />
The defined distance in both equati<strong>on</strong>s is the direct distance between transmitter <strong>and</strong> receiver terminals.<br />
Figure 5.15: Path-loss modelling in LOS c<strong>on</strong>diti<strong>on</strong>.<br />
LOS path-loss model:<br />
PL = 41.0 + 22.7⋅<br />
log10 (d), σ = 2.3dB<br />
(5.4)<br />
NLOS path-loss model:<br />
where<br />
( d , d ) PL 10n<br />
( d )<br />
PL = +<br />
, σ = 3.1 dB (5.5)<br />
1 2 0<br />
log10<br />
2<br />
PL = .096d<br />
65 <strong>and</strong> n = −0 .0024 d + 1<br />
2. 8<br />
(5.6)<br />
0<br />
0<br />
1<br />
+<br />
where d 1 <strong>and</strong> d 2 are shown in Figure 5.16. The model in Equati<strong>on</strong> (5.5) is developed in WINNER project<br />
based <strong>on</strong> measurement data fitted in [ZRKV04].<br />
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o<br />
d 1<br />
d 2<br />
d<br />
MS<br />
BS<br />
Figure 5.16: Layout of regular street grids.<br />
5.4.1.3 Scenario B3<br />
The path loss shown in Figure 5.17 was calculated at the centre-frequency of 5.2 GHz <strong>and</strong> b<strong>and</strong>width of<br />
100 MHz (measured 120 MHz) in LOS <strong>and</strong> NLOS/OLOS propagati<strong>on</strong> c<strong>on</strong>diti<strong>on</strong>s.<br />
-45<br />
-45<br />
-50<br />
-50<br />
PL [dB]<br />
PL [dB]<br />
-55<br />
-55<br />
-60<br />
-60<br />
10 1<br />
d [m]<br />
-65<br />
10 1<br />
d [m]<br />
(a)<br />
(b)<br />
Figure 5.17: Path-loss under LOS <strong>and</strong> NLOS/OLOS propagati<strong>on</strong> c<strong>on</strong>diti<strong>on</strong>.<br />
Within the scenario B3 under LOS c<strong>on</strong>diti<strong>on</strong> the equati<strong>on</strong> for the path loss was to be found as:<br />
PL = 36.9 + 13.4 log 10 (d) with s = 1.4 dB for LOS <strong>and</strong><br />
PL = 55.5 + 3.2 log 10 (d) with s = 2.1 dB for NLOS/OLOS,<br />
where d is the distance <strong>and</strong> s is the st<strong>and</strong>ard deviati<strong>on</strong> of the shadow fading.<br />
In Figure 5.18, distributi<strong>on</strong> of the shadow fading <strong>and</strong> SF versus distance are shown.<br />
0.16<br />
0.16<br />
0.14<br />
0.14<br />
0.12<br />
0.12<br />
0.1<br />
0.1<br />
PDF<br />
0.08<br />
PDF<br />
0.08<br />
0.06<br />
0.06<br />
0.04<br />
0.04<br />
0.02<br />
0.02<br />
0<br />
-4 -2 0 2 4<br />
SF [dB]<br />
0<br />
-10 -5 0 5 10<br />
SF [dB]<br />
(a)<br />
(b)<br />
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Figure 5.18: Shadow Fading distributi<strong>on</strong> in LOS (a) <strong>and</strong> NLOS (b) envir<strong>on</strong>ment.<br />
5.4.1.4 Scenario C1<br />
5.4.1.4.1 Measurements in LOS c<strong>on</strong>diti<strong>on</strong>s<br />
The path loss is shown in the figure below for the centre-frequency 5.25 GHz in LOS propagati<strong>on</strong><br />
c<strong>on</strong>diti<strong>on</strong>s. The measurements have been c<strong>on</strong>ducted in a 100 MHz b<strong>and</strong>width.<br />
Figure 5.19: Path-loss in a suburban envir<strong>on</strong>ment <strong>and</strong> LOS propagati<strong>on</strong> c<strong>on</strong>diti<strong>on</strong>s.<br />
Path-loss formula is<br />
PL(d) = 41.6 + 23.8 log 10 (d), s = 4 dB. (5.7)<br />
where d is the distance <strong>and</strong> s is the st<strong>and</strong>ard deviati<strong>on</strong> of the shadow fading.<br />
The equati<strong>on</strong> is valid from 25 m to the break-point value, see Secti<strong>on</strong> 5.6.1. From 1 m to 25 m, free-space<br />
loss formula should be used.<br />
5.4.1.5 Scenario D1<br />
5.4.1.5.1 Measurements in LOS c<strong>on</strong>diti<strong>on</strong>s<br />
In the D1 rural LOS scenario the measurements were c<strong>on</strong>ducted both in the year 2004 [D5.3] <strong>and</strong> in the<br />
year 2005. In the campaign of the year 2005, the LOS measurement was c<strong>on</strong>ducted in slightly different<br />
routes than in the previous year. Measurements were performed for three different BS locati<strong>on</strong>s, each<br />
having several measurement routes. The results are shown in the Figure 5.20.<br />
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Figure 5.20: Rural LOS path loss at 5.25 GHz.<br />
Now the equati<strong>on</strong> for the rural LOS path loss was<br />
PL(d) = 44.6 + 21.5 log 10 (d), s = 4.2 dB. (5.8)<br />
where d is the distance <strong>and</strong> s is the st<strong>and</strong>ard deviati<strong>on</strong> of the shadow fading.<br />
The equati<strong>on</strong> is valid from 30 m to the break-point value, see Secti<strong>on</strong> 5.6.1. From 1 m to 30 m, the freespace<br />
loss formula should be used.<br />
This is nearly equal to the results achieved in the campaign of the previous year. The final path-loss<br />
model for D1 LOS envir<strong>on</strong>ment as well as other path-loss <strong>models</strong> are discussed in Secti<strong>on</strong> 5.6.1.<br />
Path-loss was also investigated for l<strong>on</strong>ger distances in a separate measurement, where the base stati<strong>on</strong><br />
antenna heights were higher, 19 – 25m, <strong>and</strong> a narrower b<strong>and</strong>width was used to achieve better sensitivity.<br />
At the same time path losses for rural LOS <strong>and</strong> NLOS c<strong>on</strong>diti<strong>on</strong>s were investigated. The path-loss result<br />
for the l<strong>on</strong>gest route is shown in the Figure 5.21. The NLOS c<strong>on</strong>diti<strong>on</strong> was defined so that the path loss<br />
exceeded the free-space path loss by 10 dB or more. Three l<strong>on</strong>g routes of this kind were measured <strong>and</strong><br />
averaged to obtain the NLOS <strong>and</strong> over-all path-loss equati<strong>on</strong>s. LOS results were calculated from the<br />
short-range measurements. However, the l<strong>on</strong>g-distance measurements show clearly that very l<strong>on</strong>g LOS<br />
propagati<strong>on</strong> c<strong>on</strong>diti<strong>on</strong>s are possible in a flat envir<strong>on</strong>ment like the <strong>on</strong>e near Tyrnävä.<br />
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Figure 5.21: Path-loss in rural scenario <strong>on</strong> the route 3.1.<br />
The average corrected path-loss formula for the over-all path loss in the measurements was<br />
PL(d) = 50.4 + 25.8 log 10 (d), s = 8.4 dB (5.9)<br />
The average corrected path-loss formula for the NLOS path loss in the measurements was<br />
PL(d) = 55.8 + 25.1 log 10 (d), s = 6.7 dB (5.10)<br />
where d is the distance <strong>and</strong> s is the st<strong>and</strong>ard deviati<strong>on</strong> of the shadow fading. Correcti<strong>on</strong> means that the<br />
cutting of the high values was estimated <strong>and</strong> compensated.<br />
It should be noted that the definiti<strong>on</strong> of NLOS was performed according to the power difference of 10 dB<br />
from the free-space loss. Another note is that the noise-floor cuts the weakest signals, so that the highest<br />
path losses were cut as well. It can be assumed, however, that the effect of this limiting is relatively small.<br />
The rural NLOS measurement results were obtained from three routes, which means quite a limited set of<br />
measurements. Therefore the model has been compared with literature <strong>and</strong> adjusted appropriately in<br />
Secti<strong>on</strong> 5.6.1.<br />
5.4.2 LOS probability<br />
LOS probability is the probability that the LOS propagati<strong>on</strong> between the transmitter <strong>and</strong> the receiver<br />
exists.<br />
5.4.2.1 Scenario A1<br />
The probability of line-of-sight (LOS) propagati<strong>on</strong> vs. distance is a functi<strong>on</strong> we denote the p LOS functi<strong>on</strong>.<br />
For scenario A1, this characteristic can be derived analytically because the geometry of the scenario is<br />
known exactly.<br />
A simple ad-hoc fit of the derived p LOS functi<strong>on</strong> is given as:<br />
where x = 1 - log 10 (d / 2.5) / log 10 (100 / 2.5).<br />
5.4.2.2 Scenario B3<br />
p LOS (d) = 1 – (1 – x 3 ) 1/3 * (1 – 5 / 50), (5.11)<br />
In [LUI99] measurement results for the big factory hall envir<strong>on</strong>ment are presented. Length, width <strong>and</strong><br />
height are 90, 30 <strong>and</strong> 10 m respectively. BS height was 8 m <strong>and</strong> MS height was 1.5 m. Average<br />
probability of LOS was 0.5. Up to 10 m distance in such a big halls there is almost always LOS (if Rx is<br />
placed right). Therefore, we propose for the big factory halls, airport <strong>and</strong> train stati<strong>on</strong>s:<br />
⎧1,<br />
d < 10m<br />
P LOS<br />
= ⎨<br />
(5.12)<br />
⎩exp(<br />
−(<br />
d −10) / 45)<br />
where d is in meters. The figure below shows this functi<strong>on</strong> of the distance.<br />
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1<br />
Probability of LOS<br />
0.8<br />
0.6<br />
0.4<br />
0.2<br />
0<br />
20 40 60 80 100<br />
distance [m]<br />
Figure 5.22: Probability of LOS in factory halls, airports, <strong>and</strong> train stati<strong>on</strong>s.<br />
In the measurement campaign c<strong>on</strong>ducted at the TUI, measurements of the big lecture hall (c<strong>on</strong>ference<br />
hall) were performed. Probability of LOS was 100%. For the NLOS measurements obstructi<strong>on</strong>s were<br />
artificially made. The str<strong>on</strong>gest reas<strong>on</strong> not to have LOS would be a pers<strong>on</strong> between Tx <strong>and</strong> Rx. Since the<br />
measurements were performed when the hall was empty, the presence of some pers<strong>on</strong>s should be taken<br />
into account. If Rx is placed at the ceiling (or some other appropriate place) LOS will be almost<br />
guarantied. We propose:<br />
⎪<br />
⎧ 1, d < 5m<br />
P LOS<br />
= ⎨ d − 5<br />
1−<br />
,5 m < d < 40 m<br />
⎪⎩ 150<br />
where d is in meters. This functi<strong>on</strong> is presented in the figure below.<br />
(5.13)<br />
1<br />
Probability of LOS<br />
0.8<br />
0.6<br />
0.4<br />
0.2<br />
0<br />
5 10 15 20 25 30 35 40<br />
distance [m]<br />
Figure 5.23: Probability of LOS in lecture or c<strong>on</strong>ference halls.<br />
5.4.2.3 Scenario D1<br />
Probability of LOS in the D1 scenario is proposed to be modeled with an exp<strong>on</strong>ential functi<strong>on</strong><br />
1 d<br />
P(<br />
LOS)<br />
= exp( − )<br />
(5.14)<br />
d<br />
0<br />
d 0<br />
where d is the distance between the BS <strong>and</strong> the MS <strong>and</strong> d 0 is a c<strong>on</strong>stant defining the steepness of the<br />
exp<strong>on</strong>ential decay.<br />
Default value for d 0<br />
is proposed to be 1 km. The reas<strong>on</strong> for proposing this model is the following: It is<br />
very near the model for LOS probability defined in [3GPP SCM] at small distances. In additi<strong>on</strong> it does<br />
not go to zero at the cell boundary, so that it can be used in the <strong>system</strong>-<strong>level</strong> modelling of interference.<br />
5.4.3 DS <strong>and</strong> maximum excess-delay distributi<strong>on</strong><br />
RMS delay spread is the square root of the sec<strong>on</strong>d central moment of the PDP normalized to the total<br />
power. Max excess delay is the maximum delay after the first peak in PDP.<br />
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The measured distributi<strong>on</strong>s have been tested against the following theoretic distributi<strong>on</strong>s to find out the<br />
best fit:<br />
1) Log-normal distributi<strong>on</strong><br />
f<br />
( x − v )<br />
2<br />
1 2σ<br />
( x)<br />
=<br />
2πσ<br />
e<br />
2<br />
( x − v)<br />
, F(<br />
x)<br />
= 1−<br />
Q(<br />
)<br />
(5.15)<br />
σ<br />
2) Logistic PDF <strong>and</strong> CDF<br />
x−v<br />
ζ<br />
e<br />
f ( x)<br />
=<br />
⎛<br />
ζ ⎜1<br />
+ e<br />
⎝<br />
x−v<br />
ζ<br />
⎞<br />
⎟<br />
⎠<br />
2<br />
,<br />
F(<br />
x)<br />
= 1−<br />
where ν is the locati<strong>on</strong> parameter <strong>and</strong> scale parameter ζ > 0.<br />
1<br />
1+<br />
e<br />
x−v<br />
ζ<br />
(5.16)<br />
3) Gumbel (log of Weibull distributi<strong>on</strong>) PDF <strong>and</strong> CDF<br />
1 ⎡ x − v x − v ⎤<br />
⎛ x − v ⎞<br />
f ( x)<br />
= exp⎢−<br />
− exp( )<br />
ζ<br />
⎥ , F ( x)<br />
= exp⎜−<br />
exp( − ) ⎟ (5.17)<br />
⎣ ζ ζ ⎦<br />
⎝ ζ ⎠<br />
where ν is the locati<strong>on</strong> parameter <strong>and</strong> scale parameter ζ > 0.<br />
5.4.3.1 Scenario A1<br />
The distributi<strong>on</strong> of the RMS-delay spread was investigated. The 10, 50 <strong>and</strong> 90 % values for the<br />
Cumulative Distributi<strong>on</strong> Functi<strong>on</strong>s of RMS-delay spread are given below for the 5.25 GHz centrefrequency<br />
<strong>and</strong> different LOS <strong>and</strong> NLOS propagati<strong>on</strong> c<strong>on</strong>diti<strong>on</strong>s.<br />
Table 5.7: RMS delay spreads for A1 indoor scenario.<br />
RMS-DS (ns)<br />
Corri.-Corri. Corri.-Room Room-Room<br />
LOS NLOS NLOS LOS(OLOS)<br />
10% 15.0 13.4 13.5 9.4<br />
Percentile<br />
50% 38.0 25.2 25.1 14.2<br />
90% 75.7 48.6 40.6 18.9<br />
mean 43.0 28.5 26.5 14.2<br />
The distributi<strong>on</strong>s of the maximum excess delay were also calculated for the different envir<strong>on</strong>ments. The<br />
10, 50 <strong>and</strong> 90 % values for the Cumulative Distributi<strong>on</strong> Functi<strong>on</strong>s of the maximum excess delay are given<br />
below for the 5.25 GHz centre-frequency <strong>and</strong> different LOS <strong>and</strong> NLOS propagati<strong>on</strong> c<strong>on</strong>diti<strong>on</strong>s.<br />
Table 5.8: Maximum excess delays for the A1 indoor scenario.<br />
Corri.-Corri. Corri.-Room Room-Room<br />
Maximum excess delay range LOS NLOS NLOS LOS(OLOS)<br />
(ns)<br />
UO main building 10% 247.1 54.1 97.3 72.5<br />
widest corridor 50% 265.0 107.5 185.0 95.0<br />
3.5 m<br />
90% 624.4 249.7 255.0 130.0<br />
room size (10*10m) mean 349.0 135.0 181.8 98.1<br />
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Cumulative Distributi<strong>on</strong> Functi<strong>on</strong>s of the RMS-delay spread are given in the Figure 5.24 a <strong>and</strong> b below<br />
for the 5.25 GHz centre-frequency <strong>and</strong> c-c LOS <strong>and</strong> r-c NLOS propagati<strong>on</strong> c<strong>on</strong>diti<strong>on</strong>s. Best fit is<br />
achieved with the log-normal distributi<strong>on</strong>.<br />
a<br />
Figure 5.24: a) CDF of the A1 indoor (corridor – corridor) LOS envir<strong>on</strong>ment, fitting to normal,<br />
Gumbel <strong>and</strong> logistic distributi<strong>on</strong>s shown. b) CDF of the A1 indoor (room – corridor) NLOS<br />
envir<strong>on</strong>ment, fitting to normal distributi<strong>on</strong> shown.<br />
b<br />
5.4.3.2 Scenario B1<br />
The mean RMS delay spread for LOS <strong>and</strong> NLOS cases has been calculated for large number of <strong>channel</strong><br />
segments. The mean value of each <strong>channel</strong> segment has been calculated for data collected of every 10λ,<br />
where about five <strong>channel</strong> impulse resp<strong>on</strong>ses has been measured per wavelength. Fitting the log of the<br />
measured RMS delay spread with different distributi<strong>on</strong>s is shown in Figure 5.25. For NLOS case, the<br />
lognormal distributi<strong>on</strong> is not very close to measurement data as well as the log-logistic distributi<strong>on</strong>s. For<br />
LOS case, the Gumbel distributi<strong>on</strong> follows most of the points of the measurement CDF. The closest<br />
distributi<strong>on</strong> is the Gumbel distributi<strong>on</strong>, which is a special case of the Fisher-Tippett Distributi<strong>on</strong>. It is<br />
particularly c<strong>on</strong>venient for extreme values data. It may be used as an alternative to the normal distributi<strong>on</strong><br />
in the case of skewed empirical data.<br />
(a) LOS<br />
Figure 5.25: RMS delay spread in Scenario B1.<br />
(b) NLOS<br />
Figure 5.26 shows the maximum excess delay for both LOS <strong>and</strong> NLOS propagati<strong>on</strong> c<strong>on</strong>diti<strong>on</strong>s. Table 5.9<br />
presents mean <strong>and</strong> st<strong>and</strong>ard deviati<strong>on</strong>s of both RMS delay spread <strong>and</strong> the ZDSC excess delays for both<br />
LOS <strong>and</strong> NLOS cases.<br />
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Cumulative Probability<br />
1<br />
0.5<br />
Empirical CDF<br />
0<br />
0 500 1000 1500<br />
τ, ns<br />
Cumulative Probability<br />
1<br />
0.5<br />
Empirical CDF<br />
0<br />
0 500 1000 1500<br />
τ, ns<br />
(a) LOS<br />
Figure 5.26: ZDSC excess delay.<br />
(b) NLOS<br />
Table 5.9: Mean <strong>and</strong> st<strong>and</strong>ard deviati<strong>on</strong> of the RMS delay spread <strong>and</strong> maximum excess delay.<br />
Propagati<strong>on</strong> c<strong>on</strong>diti<strong>on</strong> LOS NLOS<br />
Centre frequencies (GHz) 5.25 5.25<br />
RMS delay spread Mean (ns) 37 74<br />
Std (ns) 25 25<br />
Max excess delay Mean (ns) 327 567<br />
Std (ns) 234 130<br />
5.4.3.3 Scenario B3<br />
The mean RMS delay spread for LOS <strong>and</strong> NLOS cases has been calculated for large number of <strong>channel</strong><br />
segments. The CDF <strong>and</strong> percentiles are presented in Figure 5.27 <strong>and</strong> Table 5.10, respectively. The mean<br />
value of each <strong>channel</strong> segment has been calculated for data set, where ten <strong>channel</strong> impulse resp<strong>on</strong>ses have<br />
been measured. The CDF <strong>and</strong> percentiles of maximum excess delay for both LOS <strong>and</strong> NLOS cases are<br />
shown in Figure 5.28 <strong>and</strong> Table 5.11 respectively.<br />
CDF<br />
1<br />
0.9<br />
0.8<br />
0.7<br />
0.6<br />
0.5<br />
0.4<br />
0.3<br />
0.2<br />
0.1<br />
0<br />
0 20 40 60 80<br />
RMS delay spread [ns]<br />
CDF<br />
1<br />
0.9<br />
0.8<br />
0.7<br />
0.6<br />
0.5<br />
0.4<br />
0.3<br />
0.2<br />
0.1<br />
0<br />
0 20 40 60 80<br />
RMS delay spread [ns]<br />
(a) LOS<br />
(b) NLOS/OLOS<br />
Figure 5.27: RMS delay spread, scenario B3.<br />
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Table 5.10: Percentiles RMS delay spread [ns].<br />
RMS delay spread (ns)<br />
LOS<br />
NLOS<br />
10% 13.2 22.3<br />
Percentile<br />
50% 23.7 37.7<br />
90% 34.6 48.9<br />
mean 23.5 36.7<br />
CDF<br />
1<br />
0.9<br />
0.8<br />
0.7<br />
0.6<br />
0.5<br />
0.4<br />
0.3<br />
0.2<br />
0.1<br />
0<br />
0 100 200 300 400<br />
Max excess delay [ns]<br />
CDF<br />
1<br />
0.9<br />
0.8<br />
0.7<br />
0.6<br />
0.5<br />
0.4<br />
0.3<br />
0.2<br />
0.1<br />
0<br />
0 100 200 300 400<br />
Max excess delay [ns]<br />
(a) LOS<br />
(b) NLOS/OLOS<br />
Figure 5.28: Maximum excess delay, scenario B3.<br />
Table 5.11: Percentiles of maximum excess delay [ns].<br />
Maximum excess delay (ns)<br />
LOS<br />
NLOS<br />
10% 83.4 116.7<br />
Percentile<br />
50% 125.0 175.1<br />
90% 175.0 249.9<br />
mean 129.3 186.1<br />
5.4.3.4 Scenario C1<br />
5.4.3.4.1 Measurements in LOS c<strong>on</strong>diti<strong>on</strong>s<br />
The distributi<strong>on</strong> of the RMS-delay spread in C1 suburban scenario was investigated. The 10, 50 <strong>and</strong> 90 %<br />
values for the Cumulative Distributi<strong>on</strong> Functi<strong>on</strong>s of the distributi<strong>on</strong> of the RMS-delay spread are given<br />
below for the 5.25 GHz centre-frequency <strong>and</strong> LOS propagati<strong>on</strong> c<strong>on</strong>diti<strong>on</strong>s.<br />
RDS (ns) 10% 50% 90% mean<br />
Suburban envir<strong>on</strong>ment 9 59 175 84<br />
Table 5.12: Percentiles of the RMS-delay spread in suburban envir<strong>on</strong>ment.<br />
5.4.3.5 Scenario D1<br />
5.4.3.5.1 Ordinary delays<br />
The The 10, 50 <strong>and</strong> 90 % percentiles of the measured RMS-delay spread are shown in the Table 5.13 for<br />
5.25 GHz in 100 MHz b<strong>and</strong>width in LOS <strong>and</strong> NLOS propagati<strong>on</strong> c<strong>on</strong>diti<strong>on</strong>s. In [5.3] it was <str<strong>on</strong>g>report</str<strong>on</strong>g>ed that<br />
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the behaviour is very similar at 2.45 <strong>and</strong> 5.25 GHz centre-frequencies. The maximum excess delays were<br />
found to be roughly two to three times higher than the RMS-delay spreads.<br />
Table 5.13: Percentiles of the RMS-delay spread in a rural envir<strong>on</strong>ment.<br />
Rms delay spread (ns) LOS NLOS<br />
10% 2.5 4.3<br />
Percentile<br />
50% 15.4 37.1<br />
90% 84.4 89.5<br />
mean 36.8 42.1<br />
5.4.3.5.2 Excepti<strong>on</strong>ally l<strong>on</strong>g delays<br />
The RMS delay spread as fucti<strong>on</strong> of distance al<strong>on</strong>g the measurement route was discussed in [5.3]. One<br />
example is shown in Figure 5.29. It can be seen that near 740 m from the start of the measurement route<br />
there is an abrupt rise of the RMS delay spread. The delay spread jumps there from some tens of<br />
nanosec<strong>on</strong>ds up to 800 ns for a short interval, about 25 m. The reas<strong>on</strong> is obviously a reflecti<strong>on</strong> from a<br />
nearby radio mast.<br />
900<br />
800<br />
700<br />
Dealay spread (ns)<br />
600<br />
500<br />
400<br />
300<br />
200<br />
100<br />
0<br />
0 100 200 300 400 500 600 700 800 900<br />
Distance (m )<br />
Figure 5.29: RMS delay spread as a functi<strong>on</strong> of distance.<br />
It should be noted that this kind of reflectors, e.g. radio masts <strong>and</strong> supporting pillars of power lines, are<br />
quite comm<strong>on</strong> in our rural envir<strong>on</strong>ments. However, the probability of reflecti<strong>on</strong>s was not possible to be<br />
estimated in our current campaign.<br />
This kind of excepti<strong>on</strong>ally delayed paths can not be modelled with the primary model with exp<strong>on</strong>entially<br />
distributed delay spreads. They have to be modelled as far clusters [SCM]. However, at the current model<br />
this kind of excepti<strong>on</strong>al phenomen<strong>on</strong> has been neglected.<br />
5.4.4 Azimuth AS at BS <strong>and</strong> MS<br />
Azimuth angle-spread is calculated like described in [3GPP SCM] from DoA <strong>and</strong> path power values. It is<br />
known as circular angle-spread. Here it is calculated at both BS <strong>and</strong> MS <strong>link</strong> end.<br />
5.4.4.1 Scenario A1<br />
The cumulative distributi<strong>on</strong> functi<strong>on</strong>s of the azimuth spreads at 5.25 GHz are shown in Figure 5.30 for<br />
LOS <strong>and</strong> NLOS propagati<strong>on</strong> c<strong>on</strong>diti<strong>on</strong>s. The percentiles for the CDF functi<strong>on</strong>s for the angle-spreads are<br />
shown in the table Table 5.14 below.<br />
Table 5.14: Percentiles of the RMS azimuth spread.<br />
Combined Corri.-Corri. LOS Corri.-Room NLOS<br />
Tietotalo & Main building Azim. Elev. Azim. Elev.<br />
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BS,<br />
MS,<br />
σ φ<br />
σ ϕ<br />
10% 2.7 4.5 10.2 6.3<br />
50% 4.8 7.6 21.5 10.9<br />
90% 16.2 13.6 39.5 21.7<br />
mean 7.0 8.4 23.0 12.9<br />
10% 14.5 4.1 24.6 7.5<br />
50% 36.6 10.1 37.4 12.1<br />
90% 67.0 15.4 56.2 21.9<br />
mean 38.8 9.9 39.3 13.5<br />
(a) LOS (Corri-Corri)<br />
(b) NLOS (Corri.-Room)<br />
Figure 5.30: Example CDFs of Azimuth spreads at BS <strong>and</strong> MS.<br />
5.4.4.2 Scenario B1<br />
Figure 5.31 shows RMS azimuth angle-spread at the MS <strong>and</strong> for LOS <strong>and</strong> NLOS propagati<strong>on</strong> c<strong>on</strong>diti<strong>on</strong>s.<br />
Figure 5.32 presents RMS azimuth angle-spread at the BS for both LOS <strong>and</strong> NLOS propagati<strong>on</strong><br />
c<strong>on</strong>diti<strong>on</strong>s. We have not made any statistical fitting comparis<strong>on</strong> based <strong>on</strong> some well known techniques<br />
like KS test. However, based <strong>on</strong> Figure 5.31 the lognormal distributi<strong>on</strong> assumpti<strong>on</strong> is not as good as the<br />
log-logistic distributi<strong>on</strong> in fitting the RMS azimuth angle-spread at the MS for the case of LOS, while<br />
Gumbel distributi<strong>on</strong> has better fitting for the NLOS case. Distributi<strong>on</strong>s fitting to the RMS azimuth spread<br />
at the BS can be seen in Figure 5.32. Again for the LOS case, the log-logistic distributi<strong>on</strong> has better<br />
fitting to measurements than the lognormal distributi<strong>on</strong>. And again for NLOS case, the Gumbel<br />
distributi<strong>on</strong> has better fitting for the NLOS case.<br />
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(a) LOS<br />
(b) NLOS<br />
Figure 5.31: RMS azimuth angle-spread at the MS.<br />
(a) LOS<br />
Figure 5.32: RMS azimuth angle-spread at the BS.<br />
(b) NLOS<br />
5.4.4.3 Scenario B3<br />
The cumulative distributi<strong>on</strong> functi<strong>on</strong>s of the RMS angle-spreads at 5.20 GHz (120 MHz b<strong>and</strong>width) are<br />
shown respectively in Figure 5.33 <strong>and</strong> Figure 5.34 for LOS <strong>and</strong> NLOS propagati<strong>on</strong> c<strong>on</strong>diti<strong>on</strong>s of the B3<br />
scenario. The RMS angle-spread is calculated using the circular angle-spread formula [3GPP SCM]. No<br />
statistical fitting comparis<strong>on</strong> based <strong>on</strong> some well known techniques like KS test is applied. The<br />
percentiles for the CDF functi<strong>on</strong>s for the angle-spreads are shown in the table below.<br />
Table 5.15: Percentiles of the RMS azimuth spread.<br />
Link end BS MS<br />
Propagati<strong>on</strong><br />
c<strong>on</strong>diti<strong>on</strong><br />
Percentile<br />
(degrees)<br />
LOS NLOS LOS NLOS<br />
10 2 5 20 14<br />
50 9 15 63 40<br />
90 18 31 100 96<br />
Prob(angular spread @BS < Abscissa)<br />
1<br />
0.8<br />
0.6<br />
0.4<br />
0.2<br />
0<br />
0 10 20 30 40 50 60 70<br />
angular spread @BS [deg]<br />
(a)<br />
Prob(angular spread @MS < Abscissa)<br />
1<br />
0.8<br />
0.6<br />
0.4<br />
0.2<br />
0<br />
0 10 20 30 40 50 60 70<br />
angular spread @MS [deg]<br />
(b)<br />
Figure 5.33: RMS angle-spreads at (a) BS (AoA) <strong>and</strong> (b) MS (AoD) for the B3 scenario under LOS<br />
propagati<strong>on</strong> c<strong>on</strong>diti<strong>on</strong>.<br />
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Prob(angular spread @BS < Abscissa)<br />
1<br />
0.8<br />
0.6<br />
0.4<br />
0.2<br />
0<br />
0 10 20 30 40 50 60 70<br />
angular spread @BS [deg]<br />
(a)<br />
Prob(angular spread @MS < Abscissa)<br />
1<br />
0.8<br />
0.6<br />
0.4<br />
0.2<br />
0<br />
0 10 20 30 40 50 60 70<br />
angular spread @MS [deg]<br />
(b)<br />
Figure 5.34: RMS angle-spreads at (a) BS (AoA) <strong>and</strong> (b) MS (AoD) for the B3 scenario under<br />
NLOS propagati<strong>on</strong> c<strong>on</strong>diti<strong>on</strong>.<br />
5.4.4.4 Scenario D1<br />
Measured angle-spread cumulative distributi<strong>on</strong> functi<strong>on</strong>s at MS <strong>and</strong> BS at 5.25 GHz are shown in Figure<br />
5.35. The percentiles for the azimuth spreads at BS <strong>and</strong> at MS are shown in the Table 5.16.<br />
Table 5.16: Percentiles of the RMS azimuth spread.<br />
Rural Tyrnävä LOS NLOS<br />
BS,<br />
MS,<br />
σ φ<br />
σ ϕ<br />
10% 10.2 5.6<br />
50% 21.9 18.0<br />
90% 36.2 34.3<br />
mean 21.7 19.5<br />
10% 8.3 6.0<br />
50% 20.3 22.3<br />
90% 37.5 36.4<br />
mean 22.4 21.9<br />
(a) LOS<br />
(b) NLOS<br />
Figure 5.35: CDF of the RMS azimuth angle-spreads at BS <strong>and</strong> MS in LOS (a) <strong>and</strong> NLOS (b)<br />
c<strong>on</strong>diti<strong>on</strong>s.<br />
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5.4.5 Distributi<strong>on</strong> of the azimuth angles of the multipath comp<strong>on</strong>ents<br />
The distributi<strong>on</strong> of the azimuth angle of arrivals <strong>and</strong> angle of departure for both LOS <strong>and</strong> NLOS<br />
propagati<strong>on</strong> c<strong>on</strong>diti<strong>on</strong>s are calculated. Those results are based <strong>on</strong> superresoluti<strong>on</strong> path parameter<br />
estimati<strong>on</strong>s or the beamform method. CDFs are presented as well as characteristic parameters as 10%,<br />
50%, 90% <strong>and</strong> mean of the distributi<strong>on</strong> are extracted.<br />
5.4.5.1 Scenario B1<br />
The azimuth angle of arrivals at the MS (receive) <strong>and</strong> angle of departure from the BS (transmitter) for<br />
both LOS <strong>and</strong> NLOS propagati<strong>on</strong> c<strong>on</strong>diti<strong>on</strong>s has been extracted from measurement data using<br />
beamforming techniques. It was noted that for LOS c<strong>on</strong>diti<strong>on</strong>s there are two propagati<strong>on</strong> mechanisms that<br />
take place for signals arrive the MS. The forward propagati<strong>on</strong>, i.e., direct propagati<strong>on</strong> directi<strong>on</strong> from BS<br />
to the MS, <strong>and</strong> the backscattering for signals that travel bey<strong>on</strong>d the MS <strong>and</strong> scatter back to the MS from<br />
the opposite directi<strong>on</strong>. These two sources of arriving signals at the MS make the modelling of the azimuth<br />
arrivals to be different. Figure 5.36 shows the cumulative probability distributi<strong>on</strong> functi<strong>on</strong> of arrival<br />
angles at the MS from both directi<strong>on</strong>s. The logistic <strong>and</strong> normal distributi<strong>on</strong>s are closer in fitting with<br />
measurement data from that with Laplacian distributi<strong>on</strong> for signals that arrive due to backscattering. In<br />
the forward propagati<strong>on</strong> case, the normal distributi<strong>on</strong> is not in good fit with measurements data. Laplacian<br />
distributi<strong>on</strong> is not in an excellent fitting with measurements but closer. For angle of departure from the<br />
BS to both LOS <strong>and</strong> NLOS cases the logistic <strong>and</strong> Laplacian are in better agreement with measurement<br />
data compared to normal distributi<strong>on</strong> as can be seen in Figure 5.37.<br />
(a) DoA due to Backscattering propagati<strong>on</strong><br />
(b) DoA from forward propagati<strong>on</strong>.<br />
Figure 5.36: Azimuth directi<strong>on</strong> of arrivals of multipath comp<strong>on</strong>ents in LOS cases.<br />
(a) DoD LOS case<br />
(b) DoD NLOS case.<br />
Figure 5.37: Azimuth directi<strong>on</strong> of departure of multipath comp<strong>on</strong>ents in LOS <strong>and</strong> NLOS cases.<br />
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5.4.5.2 Scenario B3<br />
The cumulative distributi<strong>on</strong> functi<strong>on</strong>s of the AoAs <strong>and</strong> AoDs for the multipath comp<strong>on</strong>ents at 5.20 GHz<br />
(120 MHz b<strong>and</strong>width) are shown in Figure 5.38 <strong>and</strong> Figure 5.39 for LOS <strong>and</strong> NLOS propagati<strong>on</strong><br />
c<strong>on</strong>diti<strong>on</strong>s. The estimati<strong>on</strong> results for the AoAs <strong>and</strong> the AoDs are based <strong>on</strong> the superresoluti<strong>on</strong> algorithm<br />
RIMAX [RIMAX]. No statistical fitting comparis<strong>on</strong> based <strong>on</strong> some well known techniques like KS test is<br />
applied. The percentiles for the CDF functi<strong>on</strong>s for the AoAs <strong>and</strong> AoDs are shown in the table below.<br />
Here results are shown for the LOS <strong>and</strong> NLOS propagati<strong>on</strong> case.<br />
Table 5.17: Percentiles of the distributi<strong>on</strong> of azimuth.<br />
Link end BS MS<br />
Propagati<strong>on</strong><br />
c<strong>on</strong>diti<strong>on</strong><br />
Percentile<br />
(degrees)<br />
LOS NLOS LOS NLOS<br />
10 -49.1 -41.8 -107.3 -125.5<br />
50 -1.8 -1.8 -5.5 -5.5<br />
90 38.2 38.2 110.9 114.5<br />
mean -1.3 -0.4 -0.1 -3.5<br />
1<br />
1<br />
Prob(angle @BS < Abscissa)<br />
0.8<br />
0.6<br />
0.4<br />
0.2<br />
Prob(angle @MS < Abscissa)<br />
0.8<br />
0.6<br />
0.4<br />
0.2<br />
0<br />
-150 -100 -50 0 50 100 150<br />
angle @BS [deg]<br />
0<br />
-150 -100 -50 0 50 100 150<br />
angle @MS [deg]<br />
(a)<br />
(b)<br />
Figure 5.38: CDFs of azimuth angles at (a) BS (AoA) <strong>and</strong> (b) MS (AoD) for the B3 scenario under<br />
LOS propagati<strong>on</strong> c<strong>on</strong>diti<strong>on</strong>.<br />
1<br />
1<br />
Prob(angle @BS < Abscissa)<br />
0.8<br />
0.6<br />
0.4<br />
0.2<br />
Prob(angle @MS < Abscissa)<br />
0.8<br />
0.6<br />
0.4<br />
0.2<br />
0<br />
-150 -100 -50 0 50 100 150<br />
angle @BS [deg]<br />
0<br />
-150 -100 -50 0 50 100 150<br />
angle @MS [deg]<br />
(a)<br />
(b)<br />
Figure 5.39: CDFs of azimuth angles at (a) BS (AoA) <strong>and</strong> (b) MS (AoD) for the B3 scenario under<br />
NLOS propagati<strong>on</strong> c<strong>on</strong>diti<strong>on</strong>.<br />
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5.4.6 Angle proporti<strong>on</strong>ality factor<br />
The angle proporti<strong>on</strong>ality factor (r AS ) is defined as the ratio between the st<strong>and</strong>ard deviati<strong>on</strong> of the azimuth<br />
angles of the multipath comp<strong>on</strong>ents <strong>and</strong> the RMS azimuth spread. This parameter is needed in <strong>channel</strong><br />
model.<br />
5.4.6.1 Scenario A1<br />
The angle proporti<strong>on</strong>ality factor is shown in the Table 5.18 below for an indoor (A1) envir<strong>on</strong>ment for the<br />
different LOS <strong>and</strong> NLOS propagati<strong>on</strong> c<strong>on</strong>diti<strong>on</strong>s.<br />
Table 5.18: The percentiles for the CDF of the angle proporti<strong>on</strong>ality factor.<br />
Combined<br />
Corri.-Corri. Corri.-Room Room-Room<br />
Tietotalo & Main building LOS NLOS NLOS LOS (OLOS)<br />
BS,<br />
MS,<br />
r φ<br />
r ϕ<br />
10% 0.98 0.00 0.93 1.38<br />
50% 1.40 0.78 1.11 1.72<br />
90% 1.99 1.72 1.63 2.58<br />
mean 1.45 0.99 1.22 1.90<br />
10% 1.04 0.00 1.01 0.85<br />
50% 1.44 0.81 1.31 1.26<br />
90% 1.74 2.13 1.70 1.59<br />
mean 1.41 1.64 1.33 1.25<br />
5.4.6.2 Scenario B1<br />
The r AS has been extracted for signals arrive at (or depart from) the MS both in LOS <strong>and</strong> NLOS. Figure<br />
5.40 shows the results for MS terminal in both LOS <strong>and</strong> NLOS. The corresp<strong>on</strong>ding results for the BS are<br />
shown in Figure 5.41.<br />
The percentiles for the CDF of the angle proporti<strong>on</strong>ality factor in scenario B1 at the MS <strong>and</strong> at the BS are<br />
shown in Table 5.19 <strong>and</strong> Table 5.20, respectively.<br />
1<br />
Empirical CDF<br />
1<br />
Empirical CDF<br />
CDF<br />
0.5<br />
CDF<br />
0.5<br />
0<br />
0 5 10 15<br />
0<br />
0 2 4 6 8 10<br />
r AS<br />
r AS<br />
(a) LOS case<br />
(b) NLOS<br />
Figure 5.40: Angle proporti<strong>on</strong>ality factor at the MS.<br />
Table 5.19: The percentiles for the CDF of the angle proporti<strong>on</strong>ality factor in scenario B1 at the<br />
MS.<br />
Link end<br />
MS<br />
Propagati<strong>on</strong> c<strong>on</strong>diti<strong>on</strong> LOS NLOS<br />
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r AS<br />
10% 2 2<br />
50% 4 3.5<br />
90% 8 6<br />
1<br />
Empirical CDF<br />
1<br />
Empirical CDF<br />
CDF<br />
0.5<br />
CDF<br />
0.5<br />
0<br />
0 5 10 15<br />
0<br />
0 2 4 6<br />
r AS<br />
r AS<br />
(a) LOS case<br />
(b) NLOS<br />
Figure 5.41: Angle proporti<strong>on</strong>ality factor at the BS.<br />
Table 5.20: The percentiles for the CDF of the angle proporti<strong>on</strong>ality factor in scenario B1 at the BS.<br />
Link end<br />
BS<br />
Propagati<strong>on</strong> c<strong>on</strong>diti<strong>on</strong> LOS NLOS<br />
10% 2 0.9<br />
r AS<br />
50% 4 1.1<br />
90% 7 2.2<br />
5.4.6.3 Scenario B3<br />
The angle proporti<strong>on</strong>ality factor (r AS ) has been extracted for signals arrive at (or depart from) the MS both<br />
in LOS <strong>and</strong> NLOS propagati<strong>on</strong> c<strong>on</strong>diti<strong>on</strong>s for B3 scenario. Figure 5.42 shows the results at the MS <strong>and</strong><br />
BS for LOS. The corresp<strong>on</strong>ding results for the NLOS are shown in Figure 5.43. The percentiles for the<br />
CDF of the angle proporti<strong>on</strong>ality factor in B3 scenario are shown in Table 5.21 for LOS <strong>and</strong> NLOS.<br />
1<br />
1<br />
Prob(r-factor @BS < Abscissa)<br />
0.8<br />
0.6<br />
0.4<br />
0.2<br />
Prob(r-factor @BS < Abscissa)<br />
0.8<br />
0.6<br />
0.4<br />
0.2<br />
0<br />
0 2 4 6 8 10<br />
r-factor @BS<br />
0<br />
0 2 4 6 8 10<br />
r-factor @BS<br />
(a)<br />
(b)<br />
Figure 5.42: Angle proporti<strong>on</strong>ality factor at the (a) BS <strong>and</strong> (b) MS under LOS.<br />
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1<br />
1<br />
Prob(r-factor @BS < Abscissa)<br />
0.8<br />
0.6<br />
0.4<br />
0.2<br />
Prob(r-factor @MS < Abscissa)<br />
0.8<br />
0.6<br />
0.4<br />
0.2<br />
0<br />
0 2 4 6 8 10<br />
r-factor @BS<br />
0<br />
0 2 4 6 8 10<br />
r-factor @MS<br />
(a)<br />
(b)<br />
Figure 5.43: Angle proporti<strong>on</strong>ality factor at the (a) BS <strong>and</strong> (b) MS under NLOS.<br />
Table 5.21: The percentiles for the CDF of the angle proporti<strong>on</strong>ality factor in scenario B3 at the BS<br />
<strong>and</strong> the MS, LOS <strong>and</strong> NLOS.<br />
Link end BS MS<br />
Propagati<strong>on</strong><br />
c<strong>on</strong>diti<strong>on</strong><br />
Percentile<br />
(degrees)<br />
LOS NLOS LOS NLOS<br />
10 0.6 0.7 1.0 0.8<br />
50 1.2 1.2 1.5 1.1<br />
90 2.3 2.7 2.8 1.8<br />
mean 1.5 1.6 1.9 1.3<br />
5.4.6.4 Scenario D1<br />
The angle proporti<strong>on</strong>ality factor for a rural (D1) envir<strong>on</strong>ment is shown in the Table 5.22 below for the<br />
LOS <strong>and</strong> NLOS propagati<strong>on</strong> c<strong>on</strong>diti<strong>on</strong>s.<br />
Table 5.22: The percentiles for the CDF of the angle proporti<strong>on</strong>ality factor.<br />
Rural Tyrnävä LOS NLOS<br />
BS,<br />
MS,<br />
r φ<br />
r ϕ<br />
10% 0.00 0.00<br />
50% 0.74 0.62<br />
90% 1.58 2.85<br />
mean 1.17 2.11<br />
10% 0.00 0.00<br />
50% 3.66 2.32<br />
90% 10.4 13.1<br />
mean 6.70 8.84<br />
5.4.7 Modelling of PDP<br />
Power Delay Profile (PDP) is the distributi<strong>on</strong> of the power of the multipath comp<strong>on</strong>ents versus delay<br />
time. Power delay profiles for LOS <strong>and</strong> NLOS propagati<strong>on</strong> c<strong>on</strong>diti<strong>on</strong>s have been fitted to the exp<strong>on</strong>ential<br />
functi<strong>on</strong><br />
P<br />
−bτ<br />
( τ ) e<br />
= (5.18)<br />
where τ is the excess delay <strong>and</strong> b is a time c<strong>on</strong>stant. Excess delay is difference between delays of the<br />
multipath comp<strong>on</strong>ents <strong>and</strong> the delay of the first multipath comp<strong>on</strong>ent.<br />
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5.4.7.1 Scenario A1<br />
Power delay profile at 100 MHz b<strong>and</strong>width <strong>and</strong> 5.25 GHz centre-frequency in an indoor envir<strong>on</strong>ment is<br />
shown in the figure Figure 5.44 for LOS <strong>and</strong> NLOS propagati<strong>on</strong> c<strong>on</strong>diti<strong>on</strong>s. Power delay profiles for LOS<br />
<strong>and</strong> NLOS propagati<strong>on</strong> c<strong>on</strong>diti<strong>on</strong>s have been fitted to the exp<strong>on</strong>ential functi<strong>on</strong><br />
where τ is the excess delay <strong>and</strong> b is a time c<strong>on</strong>stant.<br />
P<br />
−bτ<br />
( τ ) e<br />
= (5.19)<br />
The results are grouped in the following way: corridor to corridor (c-c) LOS, corridor to room/room to<br />
corridor (r-c) NLOS, room to room (r-r) LOS <strong>and</strong> corridor to corridor (c-c) NLOS. This grouping adapts<br />
the results more precisely to the defined A1 scenario. The results for them are shown in the table below.<br />
Table 5.23: Time c<strong>on</strong>stants for PDPs (MHz).<br />
c-c LOS c-r NLOS r-r LOS c-c NLOS<br />
50 30 69 32<br />
a<br />
Figure 5.44: Power delay profile at 100 MHz b<strong>and</strong>width <strong>and</strong> 5.25 GHz centre-frequency in an A1<br />
indoor envir<strong>on</strong>ment for corridor – corridor LOS <strong>and</strong> room – corridor NLOS propagati<strong>on</strong><br />
c<strong>on</strong>diti<strong>on</strong>s.<br />
b<br />
The peak at 250 ns delay in the Figure 5.44 a represents a reflecti<strong>on</strong> from the corridor end. If desired, it<br />
could be introduced in the model depending <strong>on</strong> the positi<strong>on</strong> of the BS. In our model we will neglect it,<br />
because of the low <strong>level</strong> of it.<br />
5.4.7.2 Scenario B1<br />
Mean measured power delay profiles (PDP) of LOS <strong>and</strong> NLOS averaged over all corresp<strong>on</strong>ding routes<br />
are shown in Figure 5.45. They are modelled <strong>and</strong> shown to be exp<strong>on</strong>ential decaying functi<strong>on</strong>.<br />
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P [dB]<br />
0<br />
-2<br />
-4<br />
-6<br />
-8<br />
-10<br />
-12<br />
-14<br />
-16<br />
-18<br />
-20<br />
0 0.2 0.4 0.6 0.8 1 1.2 1.4<br />
τ [s]<br />
x 10 -7<br />
(a) LOS<br />
P [dB]<br />
0<br />
-2<br />
-4<br />
-6<br />
-8<br />
-10<br />
-12<br />
-14<br />
-16<br />
-18<br />
-20<br />
0 1 2 3 4 5 6 7<br />
τ [s]<br />
x 10 -7<br />
(b) NLOS<br />
Figure 5.45: PDP of LOS <strong>and</strong> NLOS c<strong>on</strong>diti<strong>on</strong>s.<br />
5.4.7.3 Scenario B3<br />
Power delay profiles at 100 MHz b<strong>and</strong>width <strong>and</strong> 5.2 GHz centre-frequency in an indoor envir<strong>on</strong>ment are<br />
shown in the figure below for LOS <strong>and</strong> NLOS propagati<strong>on</strong> c<strong>on</strong>diti<strong>on</strong>s, <strong>and</strong> the 10, 50 <strong>and</strong> 90 percentiles<br />
are shown in the table.<br />
The value for b was <str<strong>on</strong>g>report</str<strong>on</strong>g>ed to be 35.7 MHz for LOS <strong>and</strong> 21.9 MHz for NLOS c<strong>on</strong>diti<strong>on</strong>s.<br />
Table 5.24: Time c<strong>on</strong>stants for PDPs (MHz).<br />
Time c<strong>on</strong>stant<br />
[MHz]<br />
LOS<br />
NLOS<br />
35.7 21.9<br />
0<br />
0<br />
-5<br />
-5<br />
Power (dB)<br />
-10<br />
-15<br />
Power (dB)<br />
-10<br />
-15<br />
-20<br />
-20<br />
-25<br />
0 50 100 150<br />
Excess delay [ns]<br />
(a) LOS<br />
-25<br />
0 50 100 150 200 250<br />
Excess delay [ns]<br />
(b) NLOS/OLOS<br />
Figure 5.46: Modeling of power delay profile (PDP) an indoor envir<strong>on</strong>ment (large) LOS (a) <strong>and</strong><br />
NLOS/OLOS (b) propagati<strong>on</strong> c<strong>on</strong>diti<strong>on</strong>s.<br />
5.4.7.4 Scenario C1<br />
Power delay profile at 100 MHz b<strong>and</strong>width <strong>and</strong> 5.25 GHz centre-frequency in a suburban envir<strong>on</strong>ment is<br />
shown in the Figure 5.47 for LOS propagati<strong>on</strong> c<strong>on</strong>diti<strong>on</strong>s. The profile has been fitted to the exp<strong>on</strong>ential<br />
functi<strong>on</strong><br />
P<br />
−bτ<br />
( τ ) e<br />
= (5.20)<br />
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where τ is the excess delay <strong>and</strong> b is a time c<strong>on</strong>stant. For the suburban LOS envir<strong>on</strong>ment the c<strong>on</strong>stant b is<br />
40 MHz.<br />
Figure 5.47: Power delay profile in the C1 (suburban) LOS envir<strong>on</strong>ment with fitting to exp<strong>on</strong>ential<br />
model.<br />
5.4.7.5 Scenario D1<br />
Power delay profiles for the rural envir<strong>on</strong>ment were investigated in 2004 <strong>and</strong> <str<strong>on</strong>g>report</str<strong>on</strong>g>ed in [D5.3] for LOS<br />
propagati<strong>on</strong> c<strong>on</strong>diti<strong>on</strong>s. In our current campaign both LOS <strong>and</strong> NLOS c<strong>on</strong>diti<strong>on</strong>s were investigated. The<br />
results of the current campaign are shown in the figure Figure 5.48. The results in [D5.3] are comparable,<br />
but less detailed.<br />
Mean PDP profiles at 100 MHz b<strong>and</strong>width <strong>and</strong> 5.25 GHz centre-frequency in a rural envir<strong>on</strong>ment are<br />
shown in the following figures.<br />
Figure 5.48: PDP profile in LOS propagati<strong>on</strong> c<strong>on</strong>diti<strong>on</strong>s.<br />
Power delay profile for LOS c<strong>on</strong>diti<strong>on</strong>s has been fitted to two segments with an exp<strong>on</strong>ential functi<strong>on</strong><br />
P<br />
−bτ<br />
( τ ) e<br />
= (5.21)<br />
where τ is the excess delay <strong>and</strong> b is the time-c<strong>on</strong>stant. Here the c<strong>on</strong>stants b 1 is 220 MHz for the first<br />
segment <strong>and</strong> b 2 is 15.6 MHz for the sec<strong>on</strong>d <strong>on</strong>e.<br />
For the D1 rural NLOS c<strong>on</strong>diti<strong>on</strong>s the PDP has been investigated in the current measurement campaign.<br />
The measured PDP can be seen in the Figure 5.49 below with dual slope <strong>and</strong> single slope fitting.<br />
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a<br />
Figure 5.49: PDP profile in NLOS propagati<strong>on</strong> c<strong>on</strong>diti<strong>on</strong>s. a) dual slope model, b) single slope<br />
model.<br />
b<br />
Here the c<strong>on</strong>stants b 1 <strong>and</strong> b 2 for the dual slope fitting are 130 MHz <strong>and</strong> 16.4 MHz, respectively. In spite<br />
of the fact that the best fit can be obtained with the dual slope profile we will use a single slope profile in<br />
the model for simplicity. Then the line fitted to the profile will have the time-c<strong>on</strong>stant b = 60 MHz.<br />
5.4.8 Number of ZDSC<br />
This sub-secti<strong>on</strong> presents number of clusters that has been extracted from measurements. The extracted<br />
clusters are based <strong>on</strong> definiti<strong>on</strong> that used in the <strong>channel</strong> model as clusters with zero delay spread. In other<br />
words, the c<strong>on</strong>sidered clustering is in angle domain. These clusters are called zero-delay-spread clusters<br />
(ZDSC). Detailed discussi<strong>on</strong> about ZDSC is given in Chapter 4.<br />
5.4.8.1 Scenario A1<br />
The distributi<strong>on</strong> of the number of clusters was investigated in an A1 indoor envir<strong>on</strong>ment. The results are<br />
shown below as the 10, 50 <strong>and</strong> 90 % percentiles of the distributi<strong>on</strong>.<br />
Table 5.25: Percentiles for the number of paths in A1 indoor scenario <strong>and</strong> different propagati<strong>on</strong><br />
c<strong>on</strong>diti<strong>on</strong>s.<br />
Number of paths<br />
Corri.-Corri. Corri.-Room Room-Room<br />
LOS NLOS NLOS LOS(OLOS)<br />
10% 8.0 5.0 4.0 5.0<br />
50% 13.0 8.0 8.0 7.0<br />
90% 19.0 15.0 14.0 9.0<br />
mean 13.0 9.0 9.0 7.0<br />
5.4.8.2 Scenario B1<br />
Figure 5.50 shows the cumulative probability of the ZDSC in both LOS <strong>and</strong> NLOS c<strong>on</strong>diti<strong>on</strong>s. Table 5.26<br />
lists the 10, 50, 90 percentiles of the empirical cumulative probability of the extracted number of ZDSCs.<br />
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1<br />
Empirical CDF<br />
1<br />
Empirical CDF<br />
0.8<br />
0.8<br />
CDF<br />
0.6<br />
0.4<br />
CDF<br />
0.6<br />
0.4<br />
0.2<br />
0.2<br />
0<br />
0 5 10 15<br />
Number of ZDSC<br />
(a) LOS<br />
0<br />
5 10 15 20 25<br />
Number of ZDSC<br />
(b) NLOS<br />
Figure 5.50: Number of ZDSC.<br />
Table 5.26: Number of extracted ZDSC.<br />
Percentile 10 50 90<br />
LOS 2 6 10<br />
NLOS 11 14 17<br />
5.4.8.3 Scenario B3<br />
The results for the number of the ZDSCs shown in are again calculated by resampling the data with 100<br />
MHz sampling rate. Table 5.27 presents the 10, 50 <strong>and</strong> 90 percentiles of the cumulative distributi<strong>on</strong> of the<br />
number of ZDSCs.<br />
CDF<br />
1<br />
0.9<br />
0.8<br />
0.7<br />
0.6<br />
0.5<br />
0.4<br />
0.3<br />
0.2<br />
0.1<br />
0<br />
0 4 8 12 16 20 24 28 32 36<br />
Number of ZDSC<br />
CDF<br />
1<br />
0.9<br />
0.8<br />
0.7<br />
0.6<br />
0.5<br />
0.4<br />
0.3<br />
0.2<br />
0.1<br />
0<br />
0 4 8 12 16 20 24 28 32 36<br />
Number of ZDSC<br />
(a) LOS<br />
(b) NLOS/OLOS<br />
Figure 5.51: Number of ZDSCs.<br />
Table 5.27: Number of ZDSCs.<br />
Number of ZDSC<br />
LOS<br />
NLOS<br />
Percentile<br />
10% 8 14<br />
50% 13 22<br />
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mean 13 22<br />
5.4.8.4 Scenario C1<br />
The distributi<strong>on</strong> of the number of clusters was investigated in a suburban C1 LOS envir<strong>on</strong>ment. The<br />
results are shown in the Table 5.28 below as the 10, 50 <strong>and</strong> 90 % percentiles of the distributi<strong>on</strong>.<br />
Table 5.28: Percentiles of the CDF of the distributi<strong>on</strong> of the number of clusters in a suburban (C1)<br />
LOS envir<strong>on</strong>ment.<br />
No. ZDSC 10% 50% 90% mean<br />
Suburban macro 3 8 22 8<br />
5.4.8.5 Scenario D1<br />
The distributi<strong>on</strong> of the number of clusters was investigated in the measurement campaign. The results are<br />
shown in the Table 5.29 as the 10, 50 <strong>and</strong> 90 % percentiles of the distributi<strong>on</strong>. The results differ from the<br />
results <str<strong>on</strong>g>report</str<strong>on</strong>g>ed in [D5.3]. The reas<strong>on</strong> is the larger number of routes measured in the latter campaign.<br />
Percentiles of the number of paths in rural envir<strong>on</strong>ment are shown in the<br />
Table 5.29: Percentiles of the number of paths in rural envir<strong>on</strong>ment.<br />
Number of paths LOS NLOS<br />
Percentile<br />
10% 1.0 1.0<br />
50% 5.0 6.0<br />
90% 17.0 14.0<br />
mean 7.4 6.7<br />
5.4.9 Distributi<strong>on</strong> of ZDSC delays<br />
Each ZDSC has a number of multipath comp<strong>on</strong>ents that differs in angle of arrivals or angle of departures<br />
but they have very close delays, i.e., multipath comp<strong>on</strong>ents that have differential delays within a chip<br />
durati<strong>on</strong> are c<strong>on</strong>sidered as <strong>on</strong>e ZDSC. Since the measurements <strong>system</strong> does not provide absolute delay,<br />
the differential delay of the ZDSCs are extracted from measurements <strong>and</strong> for both LOS <strong>and</strong> NLOS<br />
c<strong>on</strong>diti<strong>on</strong>s.<br />
5.4.9.1 Scenario A1<br />
The percentiles of the distributi<strong>on</strong> of the path delays are shown in the Table 5.30 below. The distributi<strong>on</strong><br />
can be fitted to an exp<strong>on</strong>ential distributi<strong>on</strong>, see Figure 5.52.<br />
Table 5.30: The 10, 50 <strong>and</strong> 90 % percentiles for the cumulative distributi<strong>on</strong> functi<strong>on</strong> of the path<br />
delays for an indoor envir<strong>on</strong>ment at 5.25 GHz, <strong>and</strong> different propagati<strong>on</strong> c<strong>on</strong>diti<strong>on</strong>s.<br />
Path delays<br />
Corri.-Corri. Corri.-Room Room-Room<br />
LOS NLOS NLOS LOS(OLOS)<br />
Percentile<br />
10% 11.5 0.0 0.0 0.0<br />
50% 132.5 72.5 82.5 57.5<br />
90% 376.3 217.0 220.0 122.5<br />
mean 174.5 102.8 100.6 61.7<br />
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Figure 5.52: a) Distributi<strong>on</strong>s of the path delays for the different sub-scenarios.<br />
b<br />
5.4.9.2 Scenario B1<br />
Figure 5.53 shows the empirical probability density functi<strong>on</strong> of the differential delays of the ZDSCs in<br />
LOS <strong>and</strong> NLOS cases. It can be seen that the distributi<strong>on</strong> of ZDSC delays follows exp<strong>on</strong>ential shape for<br />
LOS case <strong>and</strong> follows uniform distributi<strong>on</strong> shape in NLOS up to about 400 ns <strong>and</strong> after 400 ns it has<br />
exp<strong>on</strong>ential shape.<br />
(a) LOS<br />
(b) NLOS<br />
Figure 5.53: Empirical probability density functi<strong>on</strong>s of the ZDSC delays.<br />
5.4.9.3 Scenario B3<br />
In Figure 5.54 distributi<strong>on</strong>s of the ZDSC delays for the scenario B3 for LOS <strong>and</strong> NLOS envir<strong>on</strong>ments are<br />
presented. As expected, in NLOS case probability of ZDSC with higher delays is higher as in the LOS<br />
case.<br />
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PDF<br />
0.3<br />
0.25<br />
0.2<br />
0.15<br />
0.1<br />
0.05<br />
0<br />
0 20 40 60 80 100<br />
ZDSC delays [ns]<br />
PDF<br />
0.25<br />
0.2<br />
0.15<br />
0.1<br />
0.05<br />
0<br />
0 20 40 60 80 100<br />
ZDSC delays [ns]<br />
(a) LOS<br />
(b) NLOS<br />
Figure 5.54: Distributi<strong>on</strong>s of the ZDSC delays for the different sub-scenarios, a) LOS, b) NLOS.<br />
5.4.9.4 Scenario C1<br />
The percentiles of the distributi<strong>on</strong> of the path delays is shown in the Table 5.31.<br />
Table 5.31: Percentiles of the distributi<strong>on</strong> of the path delays in a suburban C1 LOS scenario.<br />
Path delay (ns) 10% 50% 90% mean<br />
Suburban macro 9.0 175 1525 528<br />
Also now the distributi<strong>on</strong> of the path delays fits well with the exp<strong>on</strong>ential distributi<strong>on</strong>.<br />
5.4.9.5 Scenario D1<br />
The percentiles of the CDF of the path delays are shown in Table 5.32. The measured probability density<br />
functi<strong>on</strong>s of the path delays are shown in the Figure 5.55. The distributi<strong>on</strong>s can be fitted to an exp<strong>on</strong>ential<br />
distributi<strong>on</strong> as can be seen in the figure.<br />
Table 5.32: The 10, 50 <strong>and</strong> 90 % percentiles for the cumulative distributi<strong>on</strong> functi<strong>on</strong> of the path<br />
delays for an outdoor LOS <strong>and</strong> NLOS envir<strong>on</strong>ments at 5.25 GHz.<br />
Path delay (ns)<br />
Percentile<br />
LOS<br />
NLOS<br />
10% 0 0<br />
50% 100 80<br />
90% 403 294<br />
mean 165 124<br />
The time c<strong>on</strong>stants are 140 ns for LOS <strong>and</strong> 110 ns for NLOS c<strong>on</strong>diti<strong>on</strong>s.<br />
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a<br />
Figure 5.55: a) Distributi<strong>on</strong>s of the path delays for the different sub-scenarios, a) LOS, b) NLOS.<br />
b<br />
5.4.10 Delay proporti<strong>on</strong>ality factor<br />
The delay proporti<strong>on</strong>ality factor (r DS ) is defined as the ratio between the st<strong>and</strong>ard deviati<strong>on</strong> of the delays<br />
of the multipath comp<strong>on</strong>ents <strong>and</strong> RMS delay spread.<br />
5.4.10.1 Scenario A1<br />
The delay proporti<strong>on</strong>ality factor in an A1 indoor envir<strong>on</strong>ment was calculated. The percentiles for the CDF<br />
of the delay proporti<strong>on</strong>ality factor are shown in the Table 5.33 below.<br />
Table 5.33: The 10, 50 <strong>and</strong> 90 % percentiles for the cumulative distributi<strong>on</strong> functi<strong>on</strong> of the delay<br />
proporti<strong>on</strong>ality factor in an indoor envir<strong>on</strong>ment.<br />
Delay proporti<strong>on</strong>ality factor:<br />
r τ<br />
Corri.-Corri. Corri.-Room Room-Room<br />
LOS NLOS NLOS LOS(OLOS)<br />
Percentile<br />
10% 1.9 1.5 1.7 2.4<br />
50% 3.0 2.2 2.4 3.2<br />
90% 7.5 3.9 3.2 4.2<br />
mean 3.9 2.5 2.4 3.2<br />
5.4.10.2 Scenario B1<br />
Figure 5.56 shows the empirical cumulative distributi<strong>on</strong> functi<strong>on</strong> of the proporti<strong>on</strong>ality factor both in<br />
LOS <strong>and</strong> NLOS. The median values are used as a fixed parameter in <strong>channel</strong> modelling part.<br />
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(a) LOS<br />
(b) NLOS<br />
Figure 5.56: Delay proporti<strong>on</strong>ality factor r DS .<br />
5.4.10.3 Scenario B3<br />
Figure 5.57 shows the empirical cumulative distributi<strong>on</strong> functi<strong>on</strong> of the delay proporti<strong>on</strong>ality factor in<br />
scenario B3 for both LOS <strong>and</strong> NLOS case. Percentiles of delay proporti<strong>on</strong>ality factor are given in the<br />
Table 5.34.<br />
CDF<br />
1<br />
0.9<br />
0.8<br />
0.7<br />
0.6<br />
0.5<br />
0.4<br />
0.3<br />
0.2<br />
0.1<br />
0<br />
0 1 2 3 4 5<br />
r ds<br />
(a) LOS<br />
CDF<br />
1<br />
0.9<br />
0.8<br />
0.7<br />
0.6<br />
0.5<br />
0.4<br />
0.3<br />
0.2<br />
0.1<br />
0<br />
0 1 2 3 4 5<br />
r ds<br />
(b) NLOS/OLOS<br />
Figure 5.57: Delay proporti<strong>on</strong>ality factor r DS .<br />
Table 5.34: Percentiles of delay proporti<strong>on</strong>ality factor.<br />
Delay proporti<strong>on</strong>ality factor<br />
LOS<br />
NLOS<br />
10% 1.27 1.19<br />
Percentile<br />
50% 1.80 1.58<br />
90% 2.59 1.93<br />
mean 1.90 1.58<br />
5.4.10.4 Scenario C1<br />
The percentiles for the CDF of the delay proporti<strong>on</strong>ality factor are shown in the Table 5.35 below.<br />
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Table 5.35: The 10, 50 <strong>and</strong> 90 % percentiles for the cumulative distributi<strong>on</strong> functi<strong>on</strong> of the delay<br />
proporti<strong>on</strong>ality factor in a rural envir<strong>on</strong>ment.<br />
Delay proporti<strong>on</strong>ality factor:<br />
Percentile<br />
r τ<br />
LOS<br />
NLOS<br />
10% 2.0 1.2<br />
50% 3.8 1.7<br />
90% 8.5 2.9<br />
mean 4.7 1.9<br />
5.4.10.5 Scenario D1<br />
The percentiles for the CDF of the delay proporti<strong>on</strong>ality factor for scenario D1 are presented in the Table<br />
5.36.<br />
Table 5.36: The 10, 50 <strong>and</strong> 90 % percentiles for the cumulative distributi<strong>on</strong> functi<strong>on</strong> of the delay<br />
proporti<strong>on</strong>ality factor in a rural envir<strong>on</strong>ment.<br />
delay proporti<strong>on</strong>al factor:<br />
Percentile<br />
r τ<br />
LOS NLOS<br />
10% 2.0 1.2<br />
50% 3.8 1.7<br />
90% 8.5 2.9<br />
mean 4.7 1.9<br />
5.4.11 Ricean K-factor<br />
Narowb<strong>and</strong> Ricean K factor in the LOS regi<strong>on</strong>s has been analysed. Ricean K-factor is the ratio of power<br />
of the direct LOS comp<strong>on</strong>ent to the total power of the diffused n<strong>on</strong>-line-of-sight comp<strong>on</strong>ents.<br />
5.4.11.1 Scenario A1<br />
The narrow-b<strong>and</strong> Ricean K-factor as a functi<strong>on</strong> of distance at 100 MHz b<strong>and</strong>width <strong>and</strong> 5.25 GHz centrefrequency<br />
in an indoor envir<strong>on</strong>ment is c<strong>on</strong>sidered in the corridor-corridor LOS propagati<strong>on</strong> c<strong>on</strong>diti<strong>on</strong>s.<br />
CDF of the K-factor is shown in the Figure 5.58. The fitting of the CDF with normal distributi<strong>on</strong> is<br />
shown.<br />
Figure 5.58: a) Distributi<strong>on</strong> of the K-factor in A1 indoor scenario at 5.25 GHz centre-frequency.<br />
K-factor in the A1 indoor scenario <strong>and</strong> LOS corridor to corridor propagati<strong>on</strong> c<strong>on</strong>diti<strong>on</strong>s at 5.25 GHz<br />
centre-frequency is shown in the figure Figure 5.59 as functi<strong>on</strong> of the BS – MS distance.<br />
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Figure 5.59: K-factor as functi<strong>on</strong> of distance in an A1 indoor scenario at 5.25 GHz centrefrequency.<br />
It can be seen that in this measurement the K-factor increases from 9 to 17 dB, when the distance<br />
increases from 0 to 150 m. Formula for the K-factor is<br />
K = 8.7 + 0.051 d, (5.22)<br />
where d is the distance between the BS <strong>and</strong> the MS.<br />
Table 5.37: The 10, 50 <strong>and</strong> 90 % percentiles for the cumulative distributi<strong>on</strong> functi<strong>on</strong> of the<br />
narrowb<strong>and</strong> K-factor (dB) for an indoor LOS envir<strong>on</strong>ment at 5.25 GHz.<br />
Narrowb<strong>and</strong> Ricean K-factor<br />
Percentile<br />
Corri.-Corri.<br />
LOS<br />
10% 3.0<br />
50% 12.7<br />
90% 18.4<br />
mean 11.5<br />
5.4.11.2 Scenario B1<br />
The narrowb<strong>and</strong> K-factor as functi<strong>on</strong> of distance (D) has been estimated from measurements at 5 GHz for<br />
LOS cases. The fitting linear equati<strong>on</strong> as a functi<strong>on</strong> of distance needed l<strong>on</strong>g distance measurements data.<br />
The slope (0.0142) of the following fitting equati<strong>on</strong> (5.25) is based <strong>on</strong> measurement data obtained from<br />
Nokia. The narrowb<strong>and</strong> K-factor is given in dB as<br />
K = 0 .0142D<br />
+ 3<br />
(5.23)<br />
The K-factor increases with distance in urban microcell. Near the transmitter there are several modes<br />
(multiple reflecti<strong>on</strong>s from walls), which cause low K-factor. When the distance is increased, the number<br />
of modes decrease (high-order modes have high attenuati<strong>on</strong>) <strong>and</strong> K-factor increases.<br />
5.4.11.3 Scenario B3<br />
The Ricean K factor for scenario B3 as a functi<strong>on</strong> of the distance <strong>and</strong> the CDF of it are shown in Figure<br />
5.60. The K factor decreases fast with distance inrease.<br />
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K factor [dB]<br />
20<br />
15<br />
10<br />
5<br />
0<br />
-5<br />
measurement data based<br />
linear fitting<br />
K[dB] = 6-0.26*d[m]<br />
-10<br />
0 5 10 15 20 25 30<br />
distance [m]<br />
(a)<br />
CDF<br />
1<br />
0.8<br />
0.6<br />
0.4<br />
0.2<br />
measurement<br />
based data<br />
Percentiles:<br />
10%: -2 dB<br />
50%: 1 dB<br />
90%: 4.9 dB<br />
0<br />
-10 -5 0 5 10<br />
K factor [dB]<br />
(b)<br />
Figure 5.60: Scenario B3, LOS: (a) Ricean K factor as a functi<strong>on</strong> of distance, (b) CDF of the Ricean<br />
K factor.<br />
5.4.11.4 Scenario C1<br />
The CDF percentiles of the K-factor in a suburban LOS envir<strong>on</strong>ment are given in the Table 5.38.<br />
Table 5.38: Percentiles of the CDF of the Ricean K-factor in a C1 LOS envir<strong>on</strong>ment.<br />
Percentile 10% 50% 90% mean<br />
K-factor (dB) 2.6 10.0 20.7 10.9<br />
The Ricean K-factor as a functi<strong>on</strong> of distance in a suburban LOS envir<strong>on</strong>ment is shown in the Figure<br />
5.61.<br />
Figure 5.61: Ricean K-factoe as functi<strong>on</strong> of distance in a suburban envir<strong>on</strong>ment.<br />
The equati<strong>on</strong> for the K-factor can be expressed as<br />
where d is the distance between the BS <strong>and</strong> the MS.<br />
5.4.11.5 Scenario D1<br />
K = 17.1 – 0.0205 d (5.24)<br />
The percentiles of the cumulative distributi<strong>on</strong> functi<strong>on</strong> (CDF) of the Ricean K-factor at 100 MHz<br />
b<strong>and</strong>width <strong>and</strong> 5.25 GHz centre-frequency in a rural LOS envir<strong>on</strong>ment can be found in Table 5.39.<br />
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Table 5.39: The 10, 50 <strong>and</strong> 90 % percentiles for the cumulative distributi<strong>on</strong> functi<strong>on</strong> of the<br />
narrowb<strong>and</strong> K-factor (dB) for a rural LOS envir<strong>on</strong>ment at 5.25 GHz.<br />
Percentile<br />
K-factor (dB)<br />
10% -0.6<br />
50% 10.9<br />
90% 20.0<br />
mean 10.1<br />
The cumulative distributi<strong>on</strong> functi<strong>on</strong> (CDF) of the Ricean K-factor at 100 MHz b<strong>and</strong>width <strong>and</strong> 5.25 GHz<br />
centre-frequency in a rural LOS envir<strong>on</strong>ment is shown in the figure below. It can be seen that the<br />
measured results fit quite well in log-normal distributi<strong>on</strong>. The parameters of the distributi<strong>on</strong> are: mean<br />
10.1 dB <strong>and</strong> st<strong>and</strong>ard deviati<strong>on</strong> 8.0 dB.<br />
K-factor in the D1 rural scenario <strong>and</strong> LOS propagati<strong>on</strong> c<strong>on</strong>diti<strong>on</strong>s at 5.25 GHz centre-frequency is shown<br />
in the Figure 5.62 as functi<strong>on</strong> of the BS – MS distance.<br />
Figure 5.62: Ricean K-factor as functi<strong>on</strong> of distance in a D1 rural envir<strong>on</strong>ment.<br />
In the rural LOS, we also noticed that K increases with increasing distance for the scenario D1. The<br />
formula is<br />
where d is the distance between the BS <strong>and</strong> the MS.<br />
5.4.12 Cross-polarizati<strong>on</strong> ratio (XPR)<br />
K = 3.7 + 0.019 d (5.25)<br />
The cross-polarizati<strong>on</strong> ratio XPR V is defined as the ratio of power received from vertical to vertical<br />
polarizati<strong>on</strong> to the power received from vertical to horiz<strong>on</strong>tal polarizati<strong>on</strong> as<br />
P<br />
VV<br />
XPR<br />
V<br />
= <strong>and</strong><br />
PVH<br />
P<br />
HH<br />
XPR<br />
H<br />
= (5.26)<br />
PHV<br />
Respectively, XPR H is defined as the power ratio between HH <strong>and</strong> HV comp<strong>on</strong>ents. The XPR values are<br />
extracted from the estimated propagati<strong>on</strong> paths using the str<strong>on</strong>gest path (LOS) <strong>and</strong> the reflected paths<br />
(scattering).<br />
5.4.12.1 Scenario A1<br />
The CDF percentile values of the XPR at 100 MHz b<strong>and</strong>width <strong>and</strong> 5.25 GHz centre-frequency in an<br />
indoor envir<strong>on</strong>ment is shown in the Table 5.40.<br />
Table 5.40: Percentiles of the cross-polarizati<strong>on</strong> ratio.<br />
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A1 indoor<br />
direct path scattered paths<br />
(LOS)<br />
(NLOS)<br />
10% 13.4 7.1<br />
XPR V 50% 23.2 11.2<br />
90% 31.3 15.8<br />
mean / std 22.6 / 7.7 11.4 / 3.4<br />
10% 12.3 6.2<br />
XPR H<br />
50% 18.3 10.2<br />
90% 25.3 15.1<br />
mean / std 18.7 / 5.8 10.4 / 3.4<br />
Figure 5.63: CDFs of the XPR V <strong>and</strong> XPR H in an A1 indoor envir<strong>on</strong>ment.<br />
5.4.12.2 Scenario B1<br />
The CDF of the cross-polarizati<strong>on</strong> ratio (XPR) at 100 MHz b<strong>and</strong>width <strong>and</strong> 5.25 GHz centre-frequency in<br />
a rural envir<strong>on</strong>ment is shown in the Figure 5.64 <strong>and</strong> Figure 5.65 for LOS <strong>and</strong> NLOS envir<strong>on</strong>ments,<br />
respectively. Also 10, 50 <strong>and</strong> 90% percentiles are shown in Table 5.41 <strong>and</strong> Table 5.42.<br />
Table 5.41: Cross-polarizati<strong>on</strong> ratio in LOS.<br />
P V/H [dB] XPR V [dB] XPR H [dB]<br />
Mean -1.0 8.6 9.5<br />
Median -1.2 8.7 9.8<br />
STD 2.1 1.8 2.3<br />
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Figure 5.64: Distributi<strong>on</strong> of XPR in LOS microcell: left VP <strong>and</strong> right HP.<br />
Table 5.42: Cross-polarizati<strong>on</strong> ratio in NLOS.<br />
P V/H [dB] XPR V [dB] XPR H [dB]<br />
Mean 0.4 8.0 6.9<br />
Median 0.5 7.9 6.8<br />
STD 2.5 1.8 2.8<br />
Figure 5.65: Distributi<strong>on</strong> of XPR in NLOS microcell: left VP <strong>and</strong> right HP.<br />
5.4.12.3 Scenario B3<br />
Prob(XPD)<br />
0.1<br />
0.08<br />
0.06<br />
0.04<br />
0.02<br />
Prob(XPD < Abscissa)<br />
1<br />
0.8<br />
0.6<br />
0.4<br />
0.2<br />
0<br />
-4 -2 0 2 4<br />
XPD [dB]<br />
(a)<br />
0<br />
-4 -2 0 2 4<br />
XPD [dB]<br />
(b)<br />
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Figure 5.66: XPR 1 under LOS with (a) as PDF <strong>and</strong> (b) as CDF.<br />
Prob(XPD)<br />
0.1<br />
0.08<br />
0.06<br />
0.04<br />
0.02<br />
Prob(XPD < Abscissa)<br />
1<br />
0.8<br />
0.6<br />
0.4<br />
0.2<br />
0<br />
-4 -2 0 2 4<br />
XPD [dB]<br />
(a)<br />
0<br />
-4 -2 0 2 4<br />
XPD [dB]<br />
(b)<br />
Figure 5.67: XPR 1 under NLOS with (a) as PDF <strong>and</strong> (b) as CDF.<br />
Table 5.43: Percentiles of the cross-polarizati<strong>on</strong> ratio XPR 1 .<br />
Propagati<strong>on</strong><br />
c<strong>on</strong>diti<strong>on</strong><br />
Percentile<br />
(degrees)<br />
LOS<br />
NLOS<br />
10 -1.2 -0.7<br />
50 0.7 0.1<br />
90 1.6 1.1<br />
mean 0.5 0.1<br />
The st<strong>and</strong>ard deviati<strong>on</strong> for the XPR 1 under LOS was found to be 1.07 dB <strong>and</strong> for NLOS 0.69 dB.<br />
5.4.12.4 Scenario C1<br />
5.4.12.4.1 LOS propagati<strong>on</strong> c<strong>on</strong>diti<strong>on</strong>s<br />
The CDF of the XPR values at 100 MHz b<strong>and</strong>width <strong>and</strong> 5.25 GHz centre-frequency in a suburban<br />
envir<strong>on</strong>ment is shown in the Figure 5.68.<br />
Figure 5.68: CDF’s of the XPR V <strong>and</strong> XPR H for 5.25 GHz in suburban envir<strong>on</strong>ment.<br />
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5.4.12.5 Scenario D1<br />
The CDF of the cross-polarizati<strong>on</strong> ratio (XPR) at 100 MHz b<strong>and</strong>width <strong>and</strong> 5.25 GHz centre-frequency in<br />
a rural envir<strong>on</strong>ment is shown in the Figure 5.69. The corresp<strong>on</strong>ding percentiles are listed in the Table<br />
5.44.<br />
Table 5.44: Percentiles of the cross-polarizati<strong>on</strong> ratios in a D1 rural envir<strong>on</strong>ment.<br />
D1 rural<br />
direct path<br />
(LOS)<br />
scattered paths<br />
(NLOS)<br />
10% 1.7 3.7<br />
XPR V 50% 12.2 6.3<br />
90% 20.7 9.2<br />
mean / std 11.7 / 7.8 6.4 / 2.2<br />
XPR H<br />
10% 3.2 3.2<br />
50% 13.5 6.1<br />
90% 23.3 9.1<br />
mean / std 13.2 6.1 / 2.3<br />
Figure 5.69: CDFs for the XPR V <strong>and</strong> XPR H for 5.25 GHz.<br />
5.4.13 Large-scale parameter analysis item<br />
In Secti<strong>on</strong> 3.1, the model for the so-called large-scale parameters are introduced. In the following<br />
subsecti<strong>on</strong>s the required parameters are estimated for scenario A1 LOS/NLOS, B1 LOS/NLOS, B3<br />
LOS/NOS, C1 LOS/NLOS, C2 NLOS <strong>and</strong> D1 LOS/NLOS. In all cases except A1, the vector of bulk<br />
parameters s( x, y)<br />
has four dimensi<strong>on</strong>s corresp<strong>on</strong>ding to the delay-spread, AoD spread, AoA spread <strong>and</strong><br />
log-normal shadowing. In A1, it has the additi<strong>on</strong>al dimensi<strong>on</strong> of AoD elevati<strong>on</strong> spread <strong>and</strong> AoA elevati<strong>on</strong><br />
spread. The required parameters are the vector of transformati<strong>on</strong> functi<strong>on</strong>s ~ s ( x , y)<br />
= g( s( x,<br />
y)<br />
), which<br />
s x, y into a vector ~ s ( x, y)<br />
of four Gaussian r<strong>and</strong>om variables. The mean<br />
transfoRMS the bulk vector ( )<br />
µ <strong>and</strong> correlati<strong>on</strong> R( 0)<br />
of the transformed r<strong>and</strong>om variable, <strong>and</strong> the decorrelati<strong>on</strong> distance parameters<br />
λ , K λ determine the variati<strong>on</strong> of the large-scale vector over the cell area through the equati<strong>on</strong>s<br />
1<br />
,<br />
4<br />
2<br />
E { ( x , y ) s( x y )} = R( ∆r)<br />
( ) ( ) 2<br />
R<br />
s<br />
1 1 2,<br />
2<br />
⎛<br />
⎜<br />
⎝<br />
⎛<br />
⎜<br />
⎝<br />
∆ r = x<br />
(5.27)<br />
2 − x1<br />
+ y2<br />
− y1<br />
∆r<br />
⎞ ⎛ ∆r<br />
⎞⎞<br />
⎟<br />
K<br />
⎜ ⎟⎟<br />
, (*) (5.28)<br />
λ1<br />
⎠ ⎝ λm<br />
⎠⎠<br />
0.5<br />
0.5,T<br />
( ∆r) = R ( 0) diag⎜exp⎜−<br />
⎟,<br />
,exp⎜−<br />
⎟⎟R<br />
( 0)<br />
0.5<br />
T 0.5<br />
5<br />
where R ( 0)<br />
is obtained from the eigendecompositi<strong>on</strong> R( 0) = EΛE<br />
as ( 0) = EΛ<br />
0.<br />
R .<br />
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The parameters for scenario A1, B3 <strong>and</strong> D1 have been obtained from the WINNER measurements<br />
described in Secti<strong>on</strong> 5.2.1, 5.2.3 <strong>and</strong> 5.2.6. For scenario B1, reference data from TKK outside the<br />
WINNER project is used. A combinati<strong>on</strong> of the measurements described in Secti<strong>on</strong> 5.2.4 <strong>and</strong> literature,<br />
Secti<strong>on</strong> 5.5.4, is used for scenario C1. For scenario C2, results from the reference measurement data<br />
measured by Nokia outside the WINNER project <strong>and</strong> literature (see Secti<strong>on</strong> 5.5.5) is used.<br />
For scenario A1 LOS/NLOS, C1 LOS/NLOS, C2 NLOS, D1 LOS/NLOS <strong>and</strong> Bridge2Car, all parameters<br />
are modelled as log-normal, which means that the transformati<strong>on</strong> is simply “log(x)” <strong>and</strong> the inverse<br />
“10^x”. One excepti<strong>on</strong> is the shadow fading, where the transformati<strong>on</strong> is “10log(x)”, so that the<br />
transformed variable is in dB. In scenario B1 LOS, the delay-spread is log-Gumbel, while the AoD <strong>and</strong><br />
AoA spread is log-Logistic. In B1 NLOS, the delay-spread, AoD <strong>and</strong> AoA spread are all log-Gumbel.<br />
The transformati<strong>on</strong>s for these cases are described in Secti<strong>on</strong> 3.1.1. In scenario B3, the delay-spread, AoD<br />
spread, <strong>and</strong> AoA spread are modelled as normal (thus the transformed <strong>and</strong> untransformed variables are<br />
the same). In all cases, the shadow fading is modelled as log-normal.<br />
The mean µ <strong>and</strong> covariance matrix R ( 0)<br />
have been obtained for each scenario <strong>and</strong> are listed in the tables<br />
of Secti<strong>on</strong> 3.1.1. Also listed in Secti<strong>on</strong> 3.1.1 for each scenario <strong>and</strong> parameter is a decorrelati<strong>on</strong> distance<br />
∆ . This distance has been obtained by fitting a single exp<strong>on</strong>ential exp( − ∆r / ∆)<br />
to the auto-correlati<strong>on</strong><br />
functi<strong>on</strong>s. The expressi<strong>on</strong> (*) however, mixes all of the decorrelati<strong>on</strong> distances λ<br />
1,<br />
K,λm<br />
so that we need<br />
to do a joint fit of all four auto-correlati<strong>on</strong> functi<strong>on</strong>s. Below, we have plotted the exp<strong>on</strong>ential<br />
exp( − ∆r / ∆)<br />
together with the auto-correlati<strong>on</strong> obtained from (*). The decorrelati<strong>on</strong> distances<br />
λ<br />
1,<br />
K,λ m have been manually optimized <strong>and</strong> their values are listed in Table 3.4.<br />
5.4.13.1 A1 LOS<br />
Figure 5.70: The auto correlati<strong>on</strong> functi<strong>on</strong>s obtained from (*) using the λ parameters of Table 3.4<br />
<strong>and</strong> the single exp<strong>on</strong>ential functi<strong>on</strong>s obtained from measurements in Scenario A1 LOS.<br />
5.4.13.2 A1 NLOS<br />
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Figure 5.71: The auto correlati<strong>on</strong> functi<strong>on</strong>s obtained from (*) using the λ parameters of Table 3.4<br />
<strong>and</strong> the single exp<strong>on</strong>ential functi<strong>on</strong>s obtained from measurements in Scenario A1 NLOS.<br />
5.4.13.3 B1 LOS<br />
Figure 5.72: The auto correlati<strong>on</strong> functi<strong>on</strong>s obtained from (*) using the λ parameters of Table 3.4<br />
<strong>and</strong> the single exp<strong>on</strong>ential functi<strong>on</strong>s obtained from measurements in Scenario B1 LOS.<br />
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5.4.13.4 B1 NLOS<br />
Figure 5.73: The auto correlati<strong>on</strong> functi<strong>on</strong>s obtained from (*) using the λ parameters of Table 3.4<br />
<strong>and</strong> the single exp<strong>on</strong>ential functi<strong>on</strong>s obtained from measurements in Scenario B1 NLOS.<br />
5.4.13.5 B3 LOS<br />
Figure 5.74: The auto correlati<strong>on</strong> functi<strong>on</strong>s obtained from (*) using the λ parameters of Table 3.4<br />
<strong>and</strong> the single exp<strong>on</strong>ential functi<strong>on</strong>s obtained from measurements in Scenario B3 LOS.<br />
5.4.13.6 B3 NLOS<br />
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Figure 5.75: The auto correlati<strong>on</strong> functi<strong>on</strong>s obtained from (*) using the λ parameters of Table 3.4<br />
<strong>and</strong> the single exp<strong>on</strong>ential functi<strong>on</strong>s obtained from measurements in Scenario B3 LOS.<br />
5.4.13.7 C1 LOS<br />
Figure 5.76: The auto correlati<strong>on</strong> functi<strong>on</strong>s obtained from (*) using the λ parameters of Table 3.4<br />
<strong>and</strong> the single exp<strong>on</strong>ential functi<strong>on</strong>s obtained from measurements <strong>and</strong> literature for Scenario C1<br />
LOS.<br />
5.4.13.8 C1 NLOS<br />
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Figure 5.77: The auto correlati<strong>on</strong> functi<strong>on</strong>s obtained from (*) using the λ parameters of Table 3.4<br />
<strong>and</strong> the single exp<strong>on</strong>ential functi<strong>on</strong>s obtained from measurements <strong>and</strong> literature for Scenario C1<br />
NLOS.<br />
5.4.13.9 D1 LOS<br />
Figure 5.78: The auto correlati<strong>on</strong> functi<strong>on</strong>s obtained from (*) using the λ parameters of Table 3.4<br />
<strong>and</strong> the single exp<strong>on</strong>ential functi<strong>on</strong>s obtained from measurements from Scenario D1 LOS.<br />
5.4.13.10 D1 NLOS<br />
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Figure 5.79: The auto correlati<strong>on</strong> functi<strong>on</strong>s obtained from (*) using the λ parameters of Table 3.4<br />
<strong>and</strong> the single exp<strong>on</strong>ential functi<strong>on</strong>s obtained from measurements from Scenario D1 NLOS.<br />
5.5 Literature review<br />
5.5.1 Scenario A1<br />
5.5.1.1 Path-loss<br />
In [WHL94], the indoor measurements were performed using network analyzer at 2 <strong>and</strong> 5 <strong>and</strong> 17 GHz,<br />
the RF b<strong>and</strong>width was 500 MHz. The envir<strong>on</strong>ment of the measurement locati<strong>on</strong>s was composed of a<br />
corridor of length 21.7 m, width 2 m <strong>and</strong> height 3 m <strong>and</strong> a room with dimensi<strong>on</strong>s 7 x 8 x 2.8 m. Both<br />
antennas were mounted <strong>on</strong> st<strong>and</strong>s at a height of 1.8 m.<br />
It was found that in LOS cases, the path-loss exp<strong>on</strong>ents are 1.5, 1.7, <strong>and</strong> 1.6 respectively at the three<br />
frequency b<strong>and</strong>s. There is almost no difference <strong>and</strong> <strong>on</strong>e cannot find how the path-loss exp<strong>on</strong>ents change<br />
with frequencies. However, for OLOS cases, the path-loss exp<strong>on</strong>ents were increased with the centrefrequencies.<br />
In [SG00], the measurements were performed at 5.2 GHz (RF BW was not clear) <strong>and</strong> mainly investigated<br />
the path-loss <strong>models</strong> for indoor envir<strong>on</strong>ments including the same floor corridor-corridor (LOS), corridorroom<br />
(NLOS), <strong>and</strong> room-room (NLOS) measurements <strong>and</strong> also different floor path-loss measurements.<br />
Antenna type: two patch antennas or two dipole antennas were applied.<br />
1. Same floor measurement results:<br />
office<br />
corrcorr.<br />
(LOS)<br />
corr.-<br />
room<br />
(NLOS)<br />
(NLOS)<br />
school<br />
roomroom<br />
corr.-<br />
room<br />
(NLOS)<br />
roomroom<br />
n 1.3 3.1 4.1 5.0 7.0<br />
(NLOS)<br />
PL 0 (dB) 47.4 46.1 47.9 30.6 11.3<br />
σ (dB) 2.2 2.9 2.7 2.1 3.9<br />
2. Cross floor measurement results:<br />
Horiz<strong>on</strong>tally polarized antennas:<br />
Case 1: The transmitter Tx was located at the 6th floor, then the receiver Rx was moving in a corridor at<br />
5th floor.<br />
Case 2: The Tx was at 6th floor, <strong>and</strong> the Rx was moving in vertical line between floors 0 <strong>and</strong> 5.<br />
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Case 1 Case 2<br />
n 2.4 5.6<br />
PL 0 (dB) 76.4 69.5<br />
σ (dB) 1.8 0.69<br />
The transmissi<strong>on</strong> loss due to <strong>on</strong>e floor is about 30 dB in the office building. The floor losses are not<br />
increased linearly as in Keenan-Motley model. One experiment was performed at both 5 GHz <strong>and</strong> 900<br />
MHz to determine the dependence of loss <strong>on</strong> frequencies. However, no significant difference was seen,<br />
except for the expected difference in free-space loss.<br />
In reference [YMI+04], different kind of path-loss <strong>models</strong> were obtained based <strong>on</strong> measurements<br />
performed at 5.3 GHz with RF BW 30 MHz. They can be found in the following table.<br />
5.5.1.2 Rms delay spread<br />
In [1] the mean RMS delay spread decreases with centre-frequencies as shown in the table below.<br />
The difference between frequency ranges 2-2.5 GHz <strong>and</strong> 5-5.5 GHz in the table above is quite big, the<br />
ratio is 1/3 … ½. From 5-5.5 GHz to 17-17.5 GHz the difference is much smaller, if anything.<br />
In [YMI+04], [PLN+99], [OTTH01], <strong>and</strong> [YTL02] Yacoub, D.; Teich, W.; Lindner, J., „Capacity of<br />
Vehicle-Bridge MIMO Channels”, TD(02)118, COST 273, 5th Management Committee<br />
Meeting, Lisb<strong>on</strong> / Portugal, Sep. 19-20, 2002<br />
[ZKVS02] the indoor RMS delay spread statistic values were summarized in the table<br />
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Class F [GHz] Distance (m) σ τ [ns] Value<br />
given<br />
LOS<br />
NLOS<br />
Method<br />
5.3 3-100 20-120 CDF 90% WCS [2]<br />
2.25 1-15 34.5-<br />
49.0<br />
5.25 1-15 14.4-<br />
15.7<br />
Ref<br />
mean VNA [1]<br />
mean VNA [1]<br />
5.3 5-200 30-180 CDF90% WCS [2]<br />
2.25 1-15 34.5-49 mean VNA [1]<br />
5.25 1-15 14-15.7 mean VNA [1]<br />
WCS: wideb<strong>and</strong> <strong>channel</strong> sounder.<br />
5.5.1.3 Angle-spreads<br />
Reference [DRX98] was c<strong>on</strong>sidering the tap <strong>and</strong> cluster angle-spreads of indoor WLAN <strong>channel</strong>s by<br />
using frequency domain measurements. The measured data with 400 MHz BW (5.0-5.4 GHz) were<br />
employed. FD (freq. domain)-SAGE was applied.<br />
1. Cluster <strong>and</strong> cluster AS: a cluster was based <strong>on</strong> the observati<strong>on</strong> that multipath comp<strong>on</strong>ents<br />
(MPCs) arrive in groups. AS means RMS angle-spread.<br />
2. Average tap AS: To find a tap AS for the <strong>channel</strong> with a specific delay resoluti<strong>on</strong> 1 f c , all the<br />
MPCs were collected for every 1 f c <strong>and</strong> put them in the same delay bin. For each individual<br />
tap, the instantaneous tap AS was calculated.<br />
Average tap AS <strong>and</strong> cluster AS<br />
Average tap AS with different b<strong>and</strong>widths<br />
It is interesting to notice that the mean tap AS <strong>and</strong> cluster AS have some difference, but small. The tap<br />
AS changes with RF b<strong>and</strong>width, but the difference is quite small.<br />
In [Xia96] mean azimuth spread <strong>on</strong> MS side at 5 GHz is 60.67° <strong>and</strong> st<strong>and</strong>ard deviati<strong>on</strong> is 14.26°. In the<br />
measurement setup BS antenna height was 3 m <strong>and</strong> MS antenna height 1.2 m.<br />
Measurement results from the COST 273 acti<strong>on</strong> for indoor office envir<strong>on</strong>ment are collected in [BBK+02].<br />
The following table c<strong>on</strong>tains informati<strong>on</strong> about azimuth, elevati<strong>on</strong> <strong>and</strong> delay spreads as well as about the<br />
number of identified clusters:<br />
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5.5.1.4 Spatio-temporal correlati<strong>on</strong> properties<br />
Reference [EGT+99] is about the spatio-temporal correlati<strong>on</strong> properties for 5.2 GHz indoor propagati<strong>on</strong><br />
envir<strong>on</strong>ments. The RF b<strong>and</strong>width is 120 MHz. The definiti<strong>on</strong>s of the RMS delay spread <strong>and</strong> RMS<br />
azimuth angle-spread are the same as in D5.3. The heights of both antennas were 1.8, <strong>and</strong> 2.5 m,<br />
respectively.<br />
The measurements were performed in a large room (OFF), <strong>and</strong> entrance foyer (FOY), <strong>and</strong> two corridors<br />
(corr1 was a new building, <strong>and</strong> corr2 was an old building). The linear relati<strong>on</strong>ship between RMS AS <strong>and</strong><br />
DS was found in OFF-LOS case,<br />
τ 0.84*<br />
φ −1.6<br />
(ns) (5.29)<br />
RMS<br />
=<br />
RMS<br />
In some other cases, the relati<strong>on</strong>ships cannot be fitted into linear. However, the spatio-temporal<br />
correlati<strong>on</strong> coefficient can be found in the following table.<br />
It can be seen that in all LOS cases, DS <strong>and</strong> AS have good correlati<strong>on</strong>s, however, in all NLOS cases, the<br />
correlati<strong>on</strong> coefficients are quite small.<br />
In reference [MRA93], the cluster AoAs were found to follow Gaussian distributi<strong>on</strong>, <strong>and</strong> the cluster timeof-arrivals<br />
(TOA) were found to be exp<strong>on</strong>entially distributed.<br />
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5.5.2 Scenario B3<br />
5.5.2.1 Reference data<br />
The measurement data for the large indoor scenarios were gathered with partly support by the WINNER<br />
project within a new lecture hall at the Technische Universität Ilmenau (TUI /.Germany). Measurement<br />
b<strong>and</strong>width <strong>and</strong> centre-frequency were selected to be 120 MHz <strong>and</strong> 5.2 GHz. The BS was mounted at the<br />
height of ~3.8m, whereby the transmit antenna was fixed <strong>on</strong> an automatic track with a length of 2 m<br />
between the tiers of the lecture hall. Different positi<strong>on</strong>s for the transmitter <strong>and</strong> receiver where measured.<br />
During the measurement LOS was dominating the propagati<strong>on</strong> characteristics. Furthermore<br />
measurements were performed where the LOS was obstructed.<br />
5.5.2.2 Publicati<strong>on</strong>s<br />
Large indoor envir<strong>on</strong>ment type MIMO measurements are quite rare. Most of them are not dealing with<br />
the measurement analysis items we are discussing here, but with some phenomena like distributi<strong>on</strong> of the<br />
eigenvalues etc.<br />
In [KHK+01] the indoor picocell SIMO measurements were performed in the transit hall of Helsinki<br />
airport using a spherical array of 32 dual-polarized antenna elements at 2.15 GHz b<strong>and</strong>. B<strong>and</strong>width was<br />
30 MHz. The centre of the array was at height of 1.7 m above ground <strong>level</strong>. The omnidirecti<strong>on</strong>al BS<br />
antenna was elevated at 4.6 m above the floor <strong>level</strong>, <strong>and</strong> located so that the visibility over the hall was<br />
good. The BS-MS distance varied within interval (10, 150) m.<br />
The porti<strong>on</strong> of line-of-sight (LOS) measurements was significant, of the order of 40 %.<br />
The measurement results indicate that the instantaneous cross polarizati<strong>on</strong> power ratio (XPR) is lognormally<br />
distributed. The median, mean <strong>and</strong> the st<strong>and</strong>ard deviati<strong>on</strong> of the XPR were found to be 8.8, 8.7<br />
<strong>and</strong> 5.2 dB respectively.<br />
The median, mean <strong>and</strong> the st<strong>and</strong>ard deviati<strong>on</strong> of the elevati<strong>on</strong> angle are respectively 4.1°/1.9°, 6.0°/3.2°,<br />
<strong>and</strong> 10.3°/12.2° (VP/HP).<br />
In [JXP01], large hall measurements at the Helsinki airport were performed at 5.3 GHz b<strong>and</strong>. B<strong>and</strong>width<br />
was 30 MHz. The TX antenna was at 4.55m <strong>and</strong> the RX antenna was at 1.55 m hight. Distances of up to<br />
200 m were covered.<br />
Figure 5.80: Helsinki airport hall setup<br />
For the LOS case at distances 8-100 m path-loss exp<strong>on</strong>ent <strong>and</strong> mean square error of the path loss are<br />
respectively n = 1.3 <strong>and</strong> STD = 2.0 dB.<br />
For the NLOS case at distances 35 - 200 m path-loss exp<strong>on</strong>ent <strong>and</strong> mean square error of the path loss are<br />
respectively n = 1.9 <strong>and</strong> STD = 2.7 dB.<br />
Mean K factor was 1 dB.<br />
Delay spread had values of 120 ns for the LOS case <strong>and</strong> 180 ns for the NLOS case (90% CDF).<br />
Max excess delay was around 600 ns in the NLOS case <strong>and</strong> 240 ns in the LOS case.<br />
Spatial correlati<strong>on</strong> functi<strong>on</strong> in NLOS situati<strong>on</strong> is shown in the Figure 5.81 <strong>and</strong> frequency correlati<strong>on</strong><br />
functi<strong>on</strong> in NLOS situati<strong>on</strong> is shown in the Figure 5.87, both for distances less than 30 m.<br />
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Figure 5.81: Spatial correlati<strong>on</strong> functi<strong>on</strong> for<br />
NLOS case<br />
Figure 5.82: Frequency correlati<strong>on</strong> functi<strong>on</strong>s for<br />
NLOS case<br />
In the [MET_99] Doppler power spectrums of two big hall measurements are presented in Figure 5.88<br />
<strong>and</strong> Figure 5.89. In the envir<strong>on</strong>ment named Novi3, MS was at the height 1.69 m <strong>and</strong> BS at the 2.34m<br />
height. At the Aalborg internati<strong>on</strong>al airport MS was at the height 1.69 m <strong>and</strong> BS at the 2.53m.<br />
Figure 5.83: Averadge empirical Doppler power<br />
spectrum: Novi3 - recepti<strong>on</strong> hall<br />
Figure 5.84: Averadge empirical Doppler power<br />
spectrum: Aalborg internati<strong>on</strong>al airport<br />
In [LUI99] the results of the COST 259 are presented. Big hall envir<strong>on</strong>ment is named General<br />
Factory/Hall (GFH). Length, width <strong>and</strong> height are 90, 30 <strong>and</strong> 10 m respectively. BS height was 8 m <strong>and</strong><br />
MS height was 1.5 m.<br />
Probability of LOS was 0.5. Narrowb<strong>and</strong> path-loss exp<strong>on</strong>ent was found to be 2.2. Inter cluster delay<br />
spread is 360 ns. Mean XPR is 6 dB. XPR spread is 6 dB.<br />
5.5.3 Scenario B5<br />
Note also that for the feeder scenarios we do not have any data <strong>and</strong> therefore the modelling is based<br />
entirely <strong>on</strong> the literature study. Secti<strong>on</strong> 5.5.3.1 below addresses the Doppler spectrum for fixed<br />
applicati<strong>on</strong>s, while Secti<strong>on</strong> 5.5.3.2 <strong>and</strong> 5.5.3.3 reviews publicati<strong>on</strong>s <strong>on</strong> basic parameters for the “rooftop<br />
to rooftop” <strong>and</strong> “street <strong>level</strong>” to “street-<strong>level</strong>” scenarios.<br />
5.5.3.1 Doppler for stati<strong>on</strong>ary scenarios<br />
In comm<strong>on</strong> for the feeder scenarios studied here is the assumpti<strong>on</strong> that the positi<strong>on</strong> of the transmitter <strong>and</strong><br />
receiver are fixed. In mobile-communicati<strong>on</strong>s temporal variati<strong>on</strong>s are modelled by using a moving<br />
transmitter travel through an envir<strong>on</strong>ment of fixed scatterers. In fixed applicati<strong>on</strong>s the temporal variati<strong>on</strong>s<br />
are induced by the movements of the scatterers. In [TPE02] a theoretical model is built where the change<br />
of phase of scatter between time t <strong>and</strong> t+?t is given by<br />
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f<br />
( γ ) cos( ϕ )<br />
c<br />
4π<br />
∆ t cos<br />
p p<br />
, (5.30)<br />
c<br />
where ϕ p is the angle between the directi<strong>on</strong> of scatterer movement <strong>and</strong> the directi<strong>on</strong> orthog<strong>on</strong>al to the<br />
reflecting surface <strong>and</strong> γ p the reflecti<strong>on</strong> angle. By proper selecti<strong>on</strong> of these angles different Doppler<br />
spectrums may be achieved. The results in [DGM+03] show a very narrow spectrum of <strong>on</strong>ly some 0.07<br />
Hz. In [Erc01] a much higher b<strong>and</strong>width of 5-6 Hz is proposed. We suspect that the higher b<strong>and</strong>width in<br />
[Erc01] is a worst case to account for influence from traffic.<br />
5.5.3.2 Scenario B5a - rooftop-to-rooftop<br />
The references [OBL+02], [PT00], [Dug99], [SDD00], [SCK05] treat scenarios similar to the described<br />
scenarios. In paper [OBL+02] a model based <strong>on</strong> measurements of rooftop-to-rooftop propagati<strong>on</strong> in a<br />
residential scenario at 5 GHz is presented. The transmitter antenna is a dipole <strong>and</strong> the receiver omnidirecti<strong>on</strong>al.<br />
Distances in the range 30-330 meters have been c<strong>on</strong>sidered <strong>and</strong> the LOS is sometimes<br />
obstructed by trees. A path-loss model (isotropic in dB) is derived from the measurements<br />
Loss = 46 .9 + 28log10( d)<br />
+ δ , 30m< d
WINNER D5.4 v. 1.4<br />
Figure 5.85: Comparis<strong>on</strong> of the path-loss <strong>models</strong> of [OBL+02], [PT00], free-space <strong>and</strong> a path-loss<br />
model we obtain from the results in [Dug99].<br />
In [SKE05], roof-top to roof-top MIMO measurements at 5.2 GHz are presented. Four different <strong>link</strong>s with<br />
distances of 210, 55, 180, 116 meter have been measured all with clear LOS. Measurement results include<br />
Doppler, K-factor, delay-spread, power-delay-profile, frequency correlati<strong>on</strong> <strong>and</strong> plots DoA/DoD superresoluti<strong>on</strong><br />
results from two out of the four <strong>link</strong>s. Doppler spreads of around 1 Hz at the 10 dB <strong>level</strong>. This<br />
spectrum seems to be identical in the measurements for all delay comp<strong>on</strong>ents. The K-factors measured<br />
are in the range 9.6 to 17.5 dB. The measured power delay profiles seem to be similar to a direct<br />
comp<strong>on</strong>ent plus exp<strong>on</strong>ential decay with some r<strong>and</strong>omizati<strong>on</strong>. Mean delay-spreads are in the range 6-30<br />
ns. The super-resoluti<strong>on</strong> plots show many comp<strong>on</strong>ents but most of them are very weak. A reas<strong>on</strong>able<br />
guess using <strong>on</strong>ly the plots is a power-weighted RMS delay-spread of 2 degrees.<br />
5.5.3.3 Scenario B5b - street-<strong>level</strong>-to-street-<strong>level</strong><br />
A classical two ray model with ground reflecti<strong>on</strong> results in a so-called breakpoint distance located at a<br />
distance r<br />
b given by<br />
λ<br />
h h<br />
r 4<br />
b m<br />
b<br />
= , (5.34)<br />
where hb<br />
<strong>and</strong> hm<br />
are the heights of the two ends of the <strong>link</strong>, respectively. When the distance between the<br />
two antennas is smaller than r b almost free-space path-loss is experienced. This has been observed in a<br />
number of studies [SBA+02], [OTH00], [SMI+00], [MKA02], [FBR+94] but due to reflecti<strong>on</strong>s from cars<br />
<strong>and</strong> other objects during traffic the actual breakpoint occurs at<br />
4 * (h b – h 0 ) * (h m – h 0 ) / λ (5.35)<br />
where h0<br />
is an effective ground height of typically 1.2-1.6 meters. At 5GHz we thus need 3.5 to 4 meter<br />
high antennas to achieve 380 meter free-space propagati<strong>on</strong>.<br />
In [SBA+02] <strong>and</strong> [Bal02] path loss <strong>and</strong> delay-spread measurements at 1.9 GHz <strong>and</strong> 5.8 GHz are<br />
performed in a scenario similar to what is c<strong>on</strong>sidered here. The transmitter antenna is bic<strong>on</strong>ical <strong>and</strong><br />
mounted six meters above ground in two different locati<strong>on</strong>s. The receiver antenna is omni-directi<strong>on</strong>al<br />
mounted <strong>on</strong> a minivan at 1.7 meters height <strong>and</strong> is mobile. The fading patterns at 1.9 GHz <strong>and</strong> 5.8 GHz are<br />
said to be “remarkably similar” although the measurements were not carried out simultaneously at the two<br />
frequencies. No obvious difference LOS <strong>and</strong> NLOS streets were found in teRMS of the difference in path<br />
loss between the two frequencies. The distributi<strong>on</strong> of the difference between 1.9 GHz <strong>and</strong> 5.9 GHz path<br />
loss (in dB) is said to be modelled well by a Gaussian distributi<strong>on</strong> with st<strong>and</strong>ard deviati<strong>on</strong> 4 dB for both<br />
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transmitter locati<strong>on</strong>s. The mean of the difference in <strong>on</strong>e locati<strong>on</strong> was 12 dB <strong>and</strong> for the other 7 dB. In the<br />
paper the path-loss model of [SMI+00] is found to fit the 1.9 GHz measurements <strong>on</strong> LOS streets. This<br />
model is given by<br />
PL<br />
LOS<br />
= e<br />
−sr<br />
λ<br />
π<br />
⎛<br />
⎜<br />
⎝ 4<br />
2<br />
⎞<br />
⎟<br />
⎠<br />
1<br />
e<br />
r<br />
t<br />
1<br />
+ R e<br />
r<br />
− jkrt − jkr n<br />
m<br />
2<br />
(5.36)<br />
where rt<br />
is the line-of-sight path-length, R is the reflecti<strong>on</strong> coefficient of the road surface, <strong>and</strong> s is the<br />
visibility factor. The variable r rm is the distance via reflecti<strong>on</strong> which is described as<br />
r<br />
(( h − h ) + ( h − h )) 2<br />
2<br />
rm = r +<br />
(5.37)<br />
b 0 m 0<br />
where hb<br />
<strong>and</strong> hm<br />
are the base- <strong>and</strong> mobile-stati<strong>on</strong> heights <strong>and</strong> h0<br />
is an effective surface height which is<br />
different from zero due to reflecti<strong>on</strong>s from cars <strong>and</strong> other obstacles. A best fit to the eighteen LOS streets<br />
was found to be h<br />
0 = 1.2m <strong>and</strong> s = 0.001. The RMS-error from this model in the eighteen LOS streets is<br />
listed in a table. We notice that the breakpoint distance which is based <strong>on</strong> the clearance of the first Fresnel<br />
z<strong>on</strong>e with the parameters of the paper appears at such a short distance as 60 meters. The path-loss curve is<br />
similar to a fourth-order slope bey<strong>on</strong>d the breakpoint. We calculate an average the RMS error to be 7.1<br />
dB from this data. The RMS-delay spread is <str<strong>on</strong>g>report</str<strong>on</strong>g>ed to be 15-20% lower at 5.9 GHz than at 1.9 GHz.<br />
From inspecti<strong>on</strong> of the plots in the paper it appears that for LOS cases the delay-spread is 100-150 ns<br />
quite independently of the frequency.<br />
The paper [SMI+00] presents measurements with the transmitter at a height of 4 meters <strong>and</strong> the receiver<br />
at 2.7 meters in a Japanese residential area at 3.5 GHz. The height of the buildings is <strong>on</strong> average eight<br />
meters <strong>and</strong> is therefore higher than the antennas. If the ground <strong>level</strong>, h<br />
0 , is set to zero then breakpoint<br />
distance appears at 678 meters. The measurements up to 460 meters c<strong>on</strong>firmed that free-space<br />
propagati<strong>on</strong> c<strong>on</strong>diti<strong>on</strong>s existed. Delay-spreads never exceeded 200 ns for the LOS measurements. The<br />
plotted power-delay profiles for LOS case seemed to show approximately the form of an exp<strong>on</strong>ential<br />
decay plus a direct path.<br />
The paper [MKA02] studies the impact of the traffic intensity in an urban area <strong>on</strong> the effective ground<br />
<strong>level</strong>. In the paper the base-stati<strong>on</strong> height is 4meters <strong>and</strong> the mobile-stati<strong>on</strong> height 1.6 meter or 2.7 meter.<br />
Measurements are d<strong>on</strong>e at 3.35, 8.45 <strong>and</strong> 15.75 GHz. The effective ground <strong>level</strong> is estimated to about 0.5<br />
meter during night-time <strong>and</strong> 1.4meter during daytime. The paper also presents RMS-delay-spread values<br />
versus path loss during night-time. From these figures we deduce that it is less than 200 ns at midnight<br />
before the breakpoint distance. Bey<strong>on</strong>d the breakpoint a 3.6 to 4.6 path-loss slope is observed. The<br />
st<strong>and</strong>ard deviati<strong>on</strong> around the mean value seems to be about ±5 dB before the breakpoint <strong>and</strong> ±10 dB<br />
after the breakpoint. A formula for the delay-spread is fitted to the data as<br />
s<br />
[ ns] exp( β )<br />
= , (5.38)<br />
where β is 0.050 during day-time <strong>and</strong> 0.049 during night time. The sample-points used for the fitting of<br />
this formula c<strong>on</strong>tain measurements at 3.35, 8.45 <strong>and</strong> 15.75 GHz. The paper does not state any variati<strong>on</strong><br />
with frequency.<br />
In [FBR+94] micro-cell measurements at 1900 MHz are analyzed for path loss <strong>and</strong> delay-spread with an<br />
MS height of 1.7meter <strong>and</strong> base-stati<strong>on</strong> heights of 3.7, 8.5, <strong>and</strong> 13.3. A path-loss model is fitted where a<br />
free-space propagati<strong>on</strong> law is used up to the breakpoint <strong>and</strong> a 3rd or 4th order law is recommended<br />
bey<strong>on</strong>d that point, shadow fading estimates are in the range 7-8 dB. No effective street-<strong>level</strong> modelling is<br />
used – maybe measurements were d<strong>on</strong>e when there was no traffic? An exp<strong>on</strong>ential dependence between<br />
the path loss <strong>and</strong> delay-spread as in previous reference is also found – however this time the model<br />
c<strong>on</strong>siders the maximum delay-spread. A visual inspecti<strong>on</strong> of the viewgraph of the paper seems to c<strong>on</strong>firm<br />
that typical delay-spreads obey the formula of [MKA02] given above.<br />
In [FDS+94] measurements were d<strong>on</strong>e with the transmitter at 4meter height <strong>and</strong> the receiver <strong>on</strong> the top of<br />
a Van at 2.5 meters. The measurements were d<strong>on</strong>e <strong>on</strong> Southampt<strong>on</strong> University Campus at 1.8 GHz. The<br />
results for LOS show a K-factor between 1 <strong>and</strong> 30 at range of up to the breakpoint. In c<strong>on</strong>trast for the<br />
NLOS measurements the K-factor is between 0 <strong>and</strong> 2.<br />
In [KVV05] polarizati<strong>on</strong> is analyzed in various urban scenarios. The <strong>on</strong>e, most similar to what is<br />
c<strong>on</strong>sidered here, is the urban micro-cell LOS case although the base-stati<strong>on</strong> is c<strong>on</strong>siderably more elevated<br />
than what we are c<strong>on</strong>sidering here <strong>and</strong> the mobile-stati<strong>on</strong> is less (BS height 10 meters, MS height 1.6<br />
meter in the measurements). For this scenario an XPR of around 9 dB is obtained.<br />
In [MIS01] directi<strong>on</strong>al measurements in an urban area with rotating antennas at 8.45 GHz are presented.<br />
The base-stati<strong>on</strong> height is four or eight meters while the mobile-stati<strong>on</strong> height is 3.0 meters. The angle-<br />
PL dB<br />
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spread of the main arriving wave is found to be less than 1.5 degrees in all LOS scenarios. The weaker<br />
paths in the paper seem to be virtually uniform distributed over the entire azimuth.<br />
5.5.4 Scenario C1<br />
5.5.4.1 Scenario definiti<strong>on</strong><br />
In suburban macrocells base stati<strong>on</strong>s are located well above the rooftops to allow wide area coverage.<br />
Buildings are typically low residential houses with <strong>on</strong>e or few floors. Occasi<strong>on</strong>al open areas such as parks<br />
or playgrouds between the houses make the envir<strong>on</strong>ment rather open. Streets have r<strong>and</strong>om orientati<strong>on</strong>s,<br />
<strong>and</strong> no urban-like regular strict grid structure is observed. Vegetati<strong>on</strong> is modest.<br />
5.5.4.2 Reference data<br />
WINNER suburban macrocellular measurements were made in a typical Finnish suburban residential area<br />
with rather wide streets. Buildings in the area are mainly <strong>on</strong>e- or two-storey single ore detached houses.<br />
There are open areas between the buildings, such as playgrounds, parks or small forest areas. BS height in<br />
the measurements was ~25 meters, which is well above the surrounding buildings, <strong>and</strong> at or above the<br />
height of the highest neighbouring trees. Only close to BS there were clear unobstructed LOS areas, <strong>and</strong><br />
further away the MS-BS c<strong>on</strong>necti<strong>on</strong> was obstructed mainly by trees. Deep NLOS c<strong>on</strong>diti<strong>on</strong>s were<br />
achieved at l<strong>on</strong>g MS-BS distances. Maximum measured MS-BS distances were ~1100 meters.<br />
5.5.4.3 Path loss<br />
Propagati<strong>on</strong> studies at 5 GHz frequency b<strong>and</strong> in indoor domestic, office <strong>and</strong> commercial envir<strong>on</strong>ments<br />
have been frequently <str<strong>on</strong>g>report</str<strong>on</strong>g>ed, but wideb<strong>and</strong> outdoor studies at 5 GHz are not as<br />
numerous. In [YTL02] Yacoub, D.; Teich, W.; Lindner, J., „Capacity of Vehicle-<br />
Bridge MIMO Channels”, TD(02)118, COST 273, 5th Management Committee Meeting,<br />
Lisb<strong>on</strong> / Portugal, Sep. 19-20, 2002<br />
[ZKVS02] results for urban, suburban <strong>and</strong> rural envir<strong>on</strong>ments have been <str<strong>on</strong>g>report</str<strong>on</strong>g>ed. In this case the<br />
maximum mobile (MS) to base stati<strong>on</strong> (BS) distances were limited up to 300 meters, which for outdoor<br />
cellular <strong>channel</strong> modeling is not fully representative. Path loss <strong>models</strong> around 5 GHz in residential areas<br />
<strong>and</strong> with BS heights less than 10 meters are <str<strong>on</strong>g>report</str<strong>on</strong>g>ed in [SG00] <strong>and</strong> [DRX98]<br />
More studies around 2 GHz frequency have been made. In [EGT+99] results for extensive macrocellular<br />
suburban measurements in US have been <str<strong>on</strong>g>report</str<strong>on</strong>g>ed with BS antenna heights 12…79 meters. Maximum<br />
MS-BS distances up to 8 kms were measured in variety of envir<strong>on</strong>ments c<strong>on</strong>taining both hilly <strong>and</strong> flat<br />
terrains as well as light <strong>and</strong> moderate-to-heavy tree densities. Shadow fading st<strong>and</strong>ard deviati<strong>on</strong> was<br />
found to be in range 5-16 dB, <strong>and</strong> path-loss exp<strong>on</strong>ent was always found to be greater than two. Path loss<br />
exp<strong>on</strong>ent was found to have a str<strong>on</strong>g dependency <strong>on</strong> the BS antenna height <strong>and</strong> the terrain type: the<br />
higher the BS antenna height the smaller the path-loss exp<strong>on</strong>ent.<br />
In [MRA93] <strong>and</strong> [MEJ91] radio propagati<strong>on</strong> differences between 900 <strong>and</strong> 1800 MHz centre-frequencies<br />
have been compared in different envir<strong>on</strong>ments. In both the studies it was found that path-loss values at<br />
900 <strong>and</strong> 1800 MHz were highly correlated, <strong>and</strong> there was no significant difference in distance dependent<br />
behaviour. Theoretical free-space path-loss difference between 900 <strong>and</strong> 1800 MHz is 6 dB, <strong>and</strong> in flat<br />
open areas a value very close to it, 5.7 dB, was obtained [MRA93]. In suburban areas, however, this<br />
difference was increased to 9.3 dB [MRA93], which was explained to be due to higher vegetati<strong>on</strong> in<br />
suburban envir<strong>on</strong>ments, which attenuate 1800 MHz signals more than 900 MHz signals. In [MEJ91] PL<br />
differences between 900 <strong>and</strong> 1800 MHz frequencies were found to be 9…11 dB, i.e. higher than<br />
theoretical free-space loss. In [MRA93] shadow fading st<strong>and</strong>ard deviati<strong>on</strong> was found to be approximately<br />
1 dB higher at 1800 MHz, which agrees with results from Okumura [OOKF68].<br />
In [Xia96] it has been theoretically obtained that for BS antennas above surrounding rooftop <strong>level</strong>s in<br />
suburban <strong>and</strong> urban macrocells, the path loss increases by 38 dB per decade, <strong>and</strong> with frequency by 21 dB<br />
per decade. Path loss decreases with the bases stati<strong>on</strong> antenna height, with respect to the average rooftop<br />
<strong>level</strong>, by 18 dB per decade.<br />
Ref. [BBK+02] presents wideb<strong>and</strong> <strong>channel</strong> measurements at 3.7 GHz <strong>and</strong> 20 MHz b<strong>and</strong>widh in moderate<br />
density macrocellular suburban setting outside Illinois. Reported path-loss exp<strong>on</strong>ents are between 2.9 <strong>and</strong><br />
3.4, <strong>and</strong> shadow fading st<strong>and</strong>ard deviati<strong>on</strong>s vary between 5-10 dB. Maximum measured MS-BS distances<br />
were ~ 6 kms.<br />
Effect of vegetati<strong>on</strong> has been studied in [BBK+04] <strong>and</strong> it was shown that tree foliage creates an excess<br />
path-loss of between 3 <strong>and</strong> 7 dBs.<br />
The suburban measurements in the WINNER project were made in a typical Finnish residential area with<br />
a reas<strong>on</strong>ably flat terrain, open streets <strong>and</strong> modest vegetati<strong>on</strong>. Measurements were d<strong>on</strong>e during summer<br />
time, with all trees having their full foliage. Buildings are mainly <strong>on</strong>e- or two-floor single or detached<br />
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houses surrounded by gardens or small yards. There are open areas between buildings, such as<br />
playgrounds, parks or small forest areas. Base stati<strong>on</strong> height during the measurements was ~20…25<br />
meters, which is well above the surrounding buildings, <strong>and</strong> at the height of the tallest nearby trees. MS<br />
height <strong>on</strong> top of a van was ~2 meters. Only close to BS there were clear unobstructed line-of-sight (LOS)<br />
areas, <strong>and</strong> further away MS-BS c<strong>on</strong>necti<strong>on</strong> was obstructed mainly by trees. Deep n<strong>on</strong>-line-of-sight<br />
(NLOS) propagati<strong>on</strong> c<strong>on</strong>diti<strong>on</strong>s were achieved at l<strong>on</strong>g MS-BS distances.<br />
Two different BS sites, <strong>on</strong>e of them with two sectors, were chosen, so altogether data from three different<br />
BS sectors were collected for data analysis during different measurement runs. Maximum measured BS-<br />
MS distances were ~1100 meters, <strong>and</strong> individual route length varied between 100…900 meters. More<br />
detailed descripti<strong>on</strong>s <strong>on</strong> measurements can be found in [RKJ05].<br />
The path-loss model for suburban macrocellular envir<strong>on</strong>ment obtained from WINNER measurements<br />
reads as<br />
PL = A + 10 n log10 ( d )<br />
(5.39)<br />
with n = 4.02, <strong>and</strong> A = 27.7. It is valid in distance ranges 50…1000 meters. Similar PL exp<strong>on</strong>ent values<br />
for flat macrocellular suburban envir<strong>on</strong>ment with moderate of high tree density have <str<strong>on</strong>g>report</str<strong>on</strong>g>ed in<br />
[EGT+99] around 2 GHz centre-frequency, <strong>and</strong> PL exp<strong>on</strong>ent values around 2.0-3.3 for LOS <strong>and</strong> 3.5-5.9<br />
for NLOS can be found in [SG00], <strong>and</strong> [DRX98]. However, in some of these cases the BS height, which<br />
is known to have effect <strong>on</strong> the PL behaviour, is lower than in our measurements. In our WINNER<br />
measurement we found the shadow fading comp<strong>on</strong>ent is log-normally distributed with st<strong>and</strong>ard deviati<strong>on</strong><br />
of 6.1 dB.<br />
COST231-Hata path-loss model [Cost231] for suburban macrocells is written as<br />
d<br />
PL = ( 44.9 − 6.55log<br />
10<br />
( hBS<br />
)) log<br />
10<br />
( ) + 45.5 + (35.46 −1.1h<br />
MS<br />
) log<br />
10<br />
( f<br />
c<br />
) −13.82 log<br />
10<br />
( hBS<br />
) + 0. 7h<br />
1000<br />
(5.40)<br />
In above all the distances <strong>and</strong> heights are given in meters, <strong>and</strong> centre-frequency fc is given in MHz. With<br />
h BS = 20 m, h MS = 2 m, <strong>and</strong> f c = 2000 MHz the model becomes<br />
PL = 29.6<br />
+ 36.4 log<br />
10<br />
( d )<br />
(5.41)<br />
Path loss difference due to theoretical free-space propagati<strong>on</strong> is 20*log 10 (5.3/2) = 8.5 dB. In the following<br />
figure 2 GHz COST231-Hata model with free-space path-loss correcti<strong>on</strong> <strong>and</strong> suburban macrocellular<br />
path-loss model obtained from WINNER measurements are shown. We see the difference between<br />
measurements <strong>and</strong> COST231 model is small.<br />
MS<br />
Figure 5.86: Comparis<strong>on</strong> of COST231-Hata model with free-space correcti<strong>on</strong> <strong>and</strong> path loss<br />
obtained from suburban macrocellular PL measurements in Helsinki. The measurement curve is<br />
shown <strong>on</strong>ly up to 1 km, which was the maximum measured range. COST231-Hata model is defined<br />
for distances greater than 1000 m.<br />
5.5.4.4 RMS delay spread<br />
Delay spreads around 2 GHz centre-frequency <strong>and</strong> 5 MHz b<strong>and</strong>width have been <str<strong>on</strong>g>report</str<strong>on</strong>g>ed in [AlPM02].<br />
For suburban envir<strong>on</strong>ment with BS height of 12 meters <strong>and</strong> no direct LOS between MS <strong>and</strong> BS <str<strong>on</strong>g>report</str<strong>on</strong>g>ed<br />
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delay spread values typically vary between 200…800 ns, the median being around 350 ns. Log-normal<br />
distributi<strong>on</strong> was found to give a good fit to the measured delay spread distributi<strong>on</strong>. In typical urban<br />
envir<strong>on</strong>ments delay spreads were found to decrease with increasing BS antenna height.<br />
In [WHL+93], RMS delay spread distributi<strong>on</strong>s were compared in different envir<strong>on</strong>ments at 900 MHz <strong>and</strong><br />
1900 MHz centre-frequencies. With both frequencies the used chip rate was 10 MHz, <strong>and</strong> data was<br />
recorded simultaneously with both the frequencies. It was seen that propagati<strong>on</strong> behaviour in teRMS of<br />
RMS delay spread was very similar with both the centre-frequencies in semirural, suburban <strong>and</strong> urban<br />
cells.<br />
Delay spread characteristics for 3.7 GHz centre-frequency with 20 MHz b<strong>and</strong>width are given.<br />
Measurements were made in suburban areas outside Chicago, where also some distant high-rise buildings<br />
were in the envir<strong>on</strong>ment. BS height was 49 meters, <strong>and</strong> MS was installed at 2.7 meters above the ground.<br />
15 dB dynamics criteri<strong>on</strong> from the max peak power was used in calculating delay spreads. Median delay<br />
spread values for LOS <strong>and</strong> NLOS propagati<strong>on</strong> c<strong>on</strong>diti<strong>on</strong>s were 240 <strong>and</strong> 360 ns, respectively. The<br />
combined delay spread was found to be 300 ns. As for number of rays, defined as local maxima of<br />
(instantaneous) power delay profiles, 90 percentile value of the cdf for LOS, NLOS <strong>and</strong> combined data<br />
were 3, 8 <strong>and</strong> 7 rays, respectively.<br />
In our WINNER measurements typical delay spreads were of the order of 13…125 ns, which are<br />
c<strong>on</strong>siderably smaller values than <str<strong>on</strong>g>report</str<strong>on</strong>g>ed by [AlPM02]. One reas<strong>on</strong>s for the difference is the higher BS<br />
antenna positi<strong>on</strong>. Rms delay spreads have often been <str<strong>on</strong>g>report</str<strong>on</strong>g>ed to show log-normal distributi<strong>on</strong>, as<br />
summarized for example in [GEYC]. However, instead of Gaussian, we have fitted a gumbel distributi<strong>on</strong><br />
to log10(DS), which shows a better match.<br />
5.5.4.5 Angle-spreads<br />
Azimuth spreads at BS have been given in [Pa03] for rural <strong>and</strong> suburban envir<strong>on</strong>ments at 2 GHz centrefrequency<br />
<strong>and</strong> 10 MHz b<strong>and</strong>width. The mean angle-spread of 2 degrees was found, with st<strong>and</strong>ard<br />
deviati<strong>on</strong> of 2 degrees. In urban areas they have been <str<strong>on</strong>g>report</str<strong>on</strong>g>ed to be 14 degrees <strong>and</strong> 5 degrees,<br />
respectively. The difference between the geometrical directi<strong>on</strong> of the mobile <strong>and</strong> the directi<strong>on</strong> of<br />
maximum received power was modeled as Gaussian. The st<strong>and</strong>ard deviati<strong>on</strong> is about 16 degrees near the<br />
base stati<strong>on</strong> <strong>and</strong> decreased to 8 degrees far away in urban envir<strong>on</strong>ment. In rural <strong>and</strong> suburban is much<br />
smaller, 2.7 degrees when distance is below 2 kms <strong>and</strong> above this decreases to 1.7 degrees. Mobile<br />
azimuth spreads were <str<strong>on</strong>g>report</str<strong>on</strong>g>ed as 35 degrees in suburban, <strong>and</strong> 20 degrees in rural envir<strong>on</strong>ment.<br />
5.5.5 Scenario C2<br />
5.5.5.1 Scenario definiti<strong>on</strong><br />
In urban macro cells base stati<strong>on</strong>s are located above roof tops to allow wide area coverage. Typical<br />
buildings comprise several floors (> 4) <strong>and</strong> street grids often form reguiar grid. Vegetati<strong>on</strong> is modest if<br />
any, <strong>and</strong> streets are occupied with pedestrian <strong>and</strong> vehicular traffic.<br />
5.5.5.2 Reference data<br />
Macrocellular data measured outside WINNER-project in Helsinki city centre at 5.3 GHz centrefrequency<br />
<strong>and</strong> 100 MHz chip rate was used for C2 parameter extracti<strong>on</strong>. The BS height in the<br />
measurements was ~40 meters, which is above the nearby surrounding buildings. Typical building height<br />
in the area was 4-7 stories. The measured data c<strong>on</strong>sists mostly NLOS or OLOS routes, but also some LOS<br />
secti<strong>on</strong>s in vicinity of the BS.<br />
5.5.5.3 Path loss<br />
COST231-Hata path-loss model [Cost231] is valid in frequency range from 1500-2000 MHz, BS-MS<br />
distances > 1 km, BS heights 30-200 meters <strong>and</strong> MS heights 1-10 meters. For urban macrocells the model<br />
is written as<br />
d<br />
PL = ( 44.9 − 6.55log<br />
10<br />
( hBS<br />
)) log<br />
10<br />
( ) + 48.5 + (35.46 −1.1h<br />
MS<br />
)log<br />
10<br />
( f<br />
c<br />
) −13.82 log<br />
10<br />
( hBS<br />
) + 0. 7h<br />
1000<br />
(5.42)<br />
In above all the distances <strong>and</strong> heights are given in meters, <strong>and</strong> centre-frequency f c is given in MHz. With<br />
h BS = 35 m, h MS = 2 m, <strong>and</strong> f c = 2000 MHz the model becomes<br />
PL = 31.0<br />
+ 34.8log<br />
10<br />
( d )<br />
(5.43)<br />
The path-loss model obtained from 5.3 GHz macrocellular NLOS measurements in Helsinki city centre is as follows:<br />
PL = 53.5<br />
+ 28.3log<br />
10<br />
( d),<br />
σ = 5.7<br />
(5.44)<br />
MS<br />
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It is valid in distance ranges 100…2000 meters. BS <strong>and</strong> MS heights were 35 <strong>and</strong> 2 meters, respectively.<br />
COST231-Hata model has not been originally designed to 5 GHz frequency range. Path loss difference<br />
due to theoretical free-space propagati<strong>on</strong> is 20*log 10 (5.3/2) = 8.5 dB. In the following figure 2 GHz<br />
COST231-Hata model with free-space path-loss correcti<strong>on</strong> <strong>and</strong> macrocellular NLOS path-loss model<br />
obtained from Helsinki measurements are shown.<br />
Figure 5.87: Comparis<strong>on</strong> of COST231-Hata model with free-space correcti<strong>on</strong> <strong>and</strong> path loss<br />
obtained from urban macrocellular PL measurements in Helsinki. The measurement curve is<br />
shown <strong>on</strong>ly up to 2 kms, which was the maximum measured range.<br />
It is seen that due to different PL exp<strong>on</strong>ents the differences between PL predicti<strong>on</strong>s increase with<br />
increasing MS-BS distance. At 2 kms the difference is ~7 dB. The few <str<strong>on</strong>g>report</str<strong>on</strong>g>ed outdoor PL<br />
measurements around 5 GHz frequency range show that typical PL exp<strong>on</strong>ents in urban LOS areas are<br />
close to free-space propagati<strong>on</strong> exp<strong>on</strong>ent 2 [YMI+04], [YTL02] Yacoub, D.; Teich, W.;<br />
Lindner, J., „Capacity of Vehicle-Bridge MIMO Channels”, TD(02)118, COST 273, 5th<br />
Management Committee Meeting, Lisb<strong>on</strong> / Portugal, Sep. 19-20, 2002<br />
[ZKVS02], [SG00], <strong>and</strong> for NLOS the <str<strong>on</strong>g>report</str<strong>on</strong>g>ed values vary between 3.5 <strong>and</strong> 5.8 [YMI+04], [YTL02]<br />
Yacoub, D.; Teich, W.; Lindner, J., „Capacity of Vehicle-Bridge MIMO Channels”,<br />
TD(02)118, COST 273, 5th Management Committee Meeting, Lisb<strong>on</strong> / Portugal, Sep. 19-20,<br />
2002<br />
[ZKVS02], [SG00]. In these cases the maximum MS-BS distances have been < 1000 meters.<br />
Path losses <strong>and</strong> delay spreads between 430 <strong>and</strong> 5750 MHz frequencies have been compared in [Pa05].<br />
Data was collected simultaneously at the same measurement points in multiple envir<strong>on</strong>ments, <strong>and</strong> the<br />
chip rate at each centre-frequency was 100 MHz. Measurements were made in urban envir<strong>on</strong>ment in<br />
Denver, which covered a combinati<strong>on</strong> of urban high-rise, urban low-rise <strong>and</strong> line-of sight propagati<strong>on</strong><br />
paths. BS was installed <strong>on</strong> top of a five floor building at 17 meters, <strong>and</strong> maximum measured distances in<br />
the case were up to 5 km. It was observed that line-of sight near the BS (100…300 meters) the path-loss<br />
exp<strong>on</strong>ent was close to 2. In regi<strong>on</strong>s where radio paths become obstructed the path-loss exp<strong>on</strong>ents were<br />
increasing, <strong>and</strong> they also showed frequency dependency: path-loss exp<strong>on</strong>ent increased from 4.3 to 5.4<br />
between 430 <strong>and</strong> 5750 MHz. The shadow fading was normally distributed, <strong>and</strong> ranged between 3 <strong>and</strong> 6<br />
dB.<br />
In [MRA93] <strong>and</strong> [MEJ91] radio propagati<strong>on</strong> differences between 900 <strong>and</strong> 1800 MHz centre-frequencies<br />
have been compared in different envir<strong>on</strong>ments. In both the studies it was found that path-loss values at<br />
900 <strong>and</strong> 1800 MHz were highly correlated, <strong>and</strong> there was no significant difference in distance dependent<br />
behaviour.<br />
Path loss <strong>and</strong> delay spread results from Japanese urban metropolitan envir<strong>on</strong>ment at 3 GHz, 8 GHz, <strong>and</strong><br />
15 GHz frequencies are <str<strong>on</strong>g>report</str<strong>on</strong>g>ed in [OTTH01]. The average building heights in were 20…30 meters, <strong>and</strong><br />
BS height both clearly above (macrocell) <strong>and</strong> at rooftop <strong>level</strong> (microcell) were measured. In the<br />
measurements power delay profiles were recorded simultaneously for each of the three frequencies.<br />
Shadow fading st<strong>and</strong>ard deviati<strong>on</strong>s did not show c<strong>on</strong>siderable differences between frequencies, but values<br />
in the range 5…10 dB were obtained. Path loss frequency dependency was found to directly follow freespace<br />
characteristics, i.e. 20log(f). Path loss exp<strong>on</strong>ents were not <str<strong>on</strong>g>report</str<strong>on</strong>g>ed.<br />
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A path-loss model for <strong>system</strong> simulati<strong>on</strong>s is needed for c<strong>on</strong>siderably greater distances than generally<br />
<str<strong>on</strong>g>report</str<strong>on</strong>g>ed in literature, <strong>and</strong> obtained from Helsinki PL measurements. Therefore, based <strong>on</strong> the widely<br />
varying informati<strong>on</strong> available from existing literature <strong>on</strong> propagati<strong>on</strong> at 5 GHz frequency range, we<br />
propose to use 2 GHz COST231-Hata model with free-space correcti<strong>on</strong> to model path loss around 5 GHz.<br />
A reas<strong>on</strong>able resemblance was achieved at least within 2 kilometer range with urban macrocellular<br />
measurements in Helsinki.<br />
5.5.5.4 RMS delay spread<br />
Ref. [WHL94] presents wideb<strong>and</strong> propagati<strong>on</strong> measurements taken in the 1850-1990 MHz b<strong>and</strong> with 10<br />
MHz chip rate in flat rural, hilly rural <strong>and</strong> urban high-rise envir<strong>on</strong>ments. In flat rural scenario typical<br />
delay spreads are of the order of ~100 ns, whereas in urban high-rise cells median delay spread was ~700<br />
ns. Typical delay spread values of 800-1200 ns in urban macrocellular envir<strong>on</strong>ments at 1.8 GHz are<br />
<str<strong>on</strong>g>report</str<strong>on</strong>g>ed in [AlPM02].<br />
In comparis<strong>on</strong> of path losses <strong>and</strong> delay spreads between 430 <strong>and</strong> 5750 MHz frequencies [Pa05] it was<br />
found that the delay spread decreased at higher frequencies: in urban macrocellular envir<strong>on</strong>ment the<br />
median delay spread decreased from 700 to 300 ns. Smaller delay spreads at higher frequencies indicate<br />
reflected signals were attenuated <strong>and</strong> fell below the 20 dB cutoff used for delay spread calculati<strong>on</strong>s. Also<br />
results <strong>on</strong> microcellular envir<strong>on</strong>ment <str<strong>on</strong>g>report</str<strong>on</strong>g>ed in [Bul03] show that RMS delay spreads were c<strong>on</strong>sistently<br />
lower at 6 GHz compared to those at 2 GHz by factor of 0.86.<br />
Somewhat c<strong>on</strong>tradictory c<strong>on</strong>clusi<strong>on</strong>s have been <str<strong>on</strong>g>report</str<strong>on</strong>g>ed in [OTTH01], which presents path-loss <strong>and</strong><br />
delay spread results from Japanese urban metropolitan envir<strong>on</strong>ment at 3 GHz, 8 GHz <strong>and</strong> 15 GHz<br />
frequencies. In the measurements power delay profiles were recorded simultaneously for each of the three<br />
frequencies in micro- <strong>and</strong> macrocellular scenarios. For multipath characterizati<strong>on</strong> 50 MHz b<strong>and</strong>width was<br />
used, <strong>and</strong> 15 dB dynamic range was applied in delay spread calculati<strong>on</strong>s. Typical (median) RMS delay<br />
spread values were ~100 ns, <strong>and</strong> maximum excess delays (50%) ~300 ns. Differences between<br />
frequencies were found to be very small.<br />
The following table summarizes the RMS delay spread <strong>and</strong> maximum excess delay statistics extracted<br />
from urban macrocellular measurements in Helsinki city centre. Dynamic range of 20 dB was used.<br />
10% 50% 90%<br />
σ τ [ns] 85 265 532<br />
Max excess delay [ns] 575 2210 3490<br />
5.5.5.5 Angle-spread<br />
In [PLN+99] directi<strong>on</strong>al wideb<strong>and</strong> <strong>channel</strong> measurements at 2.1 GHz centre-frequency <strong>and</strong> 50 MHz<br />
b<strong>and</strong>width in urban <strong>and</strong> suburban areas have been performed. It was found that in urban areas a BS<br />
antenna installed at lamppost <strong>level</strong> lead to more severe azimuth spread than a BS at rooftop <strong>level</strong>.<br />
Correlati<strong>on</strong> between angle-spread <strong>and</strong> delay spread was low. In urban city envir<strong>on</strong>ment the macrocellular<br />
BS positi<strong>on</strong> was at 25 meters, which is slightly above surrounding rooftop <strong>level</strong>s. BS-MS distances ranges<br />
from 20 to 360 meters. In suburban envir<strong>on</strong>ment with low residential wooden houses the BS height was 7<br />
meters, which was around the rooftop <strong>level</strong> of most the surrounding buildings. In this scenario BS-MS<br />
distances were 50…510 meters. Typical azimuth spread values (50 precentile value in cdf) in urban<br />
macrocellular envir<strong>on</strong>ment were 7.6…11.8 degrees, with mean value of 9.9 degrees. For the same<br />
envir<strong>on</strong>ment typical delay delay spreads were 20…92 ns, with mean value of 56 ns. In suburban<br />
measurements azimuth spread values 12.9…18.4 degrees with mean value of 15 degrees were obtained.<br />
Corresp<strong>on</strong>ding delay spread values were 45…233 ns, with mean 119 ns.<br />
In [KRB00] angle power distributi<strong>on</strong>s at the MS were measured in urban macrocellular envir<strong>on</strong>ment in<br />
Paris at 890 MHz. It was found that street cany<strong>on</strong>s force the l<strong>on</strong>g-delayed waves to come from street<br />
directi<strong>on</strong>s, but street crossings can cause additi<strong>on</strong>al signal comp<strong>on</strong>ents. For smaller delays local scatterers<br />
c<strong>on</strong>tribute to power spectra. Propagati<strong>on</strong> over the roofs was significant: typically 65% of energy was<br />
incident with elevati<strong>on</strong> angles larger than 10 degrees. In 1.8 GHz measurements <str<strong>on</strong>g>report</str<strong>on</strong>g>ed in [AlPM02]<br />
median angle-spreads at BS are in the range 8-13 degrees.<br />
In [KSL+02] elevati<strong>on</strong> angle distributi<strong>on</strong>s at the mobile stati<strong>on</strong> in different radio propagati<strong>on</strong><br />
envir<strong>on</strong>ments have been <str<strong>on</strong>g>report</str<strong>on</strong>g>ed at 2.15 GHz centre-frequency. Results show that in n<strong>on</strong>-line-of-sight<br />
situati<strong>on</strong>s, the power distributi<strong>on</strong> in elevati<strong>on</strong> has a shape of a double-sided exp<strong>on</strong>ential functi<strong>on</strong>, with<br />
different slopes in the negative <strong>and</strong> positive sides of the peak. The slopes <strong>and</strong> the peak elevati<strong>on</strong> angle<br />
depend in the envir<strong>on</strong>ment <strong>and</strong> BS antenna height. In urban macrocells mean elevati<strong>on</strong> angles of arrival<br />
are ~7…14 degrees, with st<strong>and</strong>ard deviati<strong>on</strong>s of 12…18 degrees.<br />
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5.5.6 Scenario D1<br />
5.5.6.1 Path-loss<br />
5.5.6.1.1 LOS path-loss<br />
The basic theoretical equati<strong>on</strong> for LOS path-loss is<br />
where d is the distance between BS <strong>and</strong> MS <strong>and</strong> A <strong>and</strong> B are c<strong>on</strong>stants.<br />
PL = A + B*log 10 (d) (5.45)<br />
Normally, A <strong>and</strong> B are near the free-space values. For example, in our measurements at 5.25 GHz the<br />
values were: A = 41.8 <strong>and</strong> B = 22.<br />
The model in (5.34) can be extended until so called break-point distance, which depends <strong>on</strong> the wavelength<br />
? <strong>and</strong> base stati<strong>on</strong> <strong>and</strong> mobile stati<strong>on</strong> antenna heights, h BS <strong>and</strong> h MS respectively [28].<br />
d BP = 4 · h BS · h MS / ? (5.46)<br />
where h BS is the height of the base stati<strong>on</strong>, h MS is the height of the mobile stati<strong>on</strong>, <strong>and</strong> ? is the wave length<br />
at f c .<br />
After this break-point, the loss is proporti<strong>on</strong>al to another, greater path-loss exp<strong>on</strong>ent. By flat earth theory,<br />
this exp<strong>on</strong>ent should be 4, but in practice it can be also greater. The model is based <strong>on</strong> the assumpti<strong>on</strong><br />
about two rays arriving at the receiver antenna, <strong>on</strong>e direct ray, the other <strong>on</strong>e reflected from the flat earth.<br />
This model is also called two-ray model.<br />
The model can be written in the form [28]:<br />
PL = A + B log 10 (d), d d BP (5.48)<br />
where C = 10 n, <strong>and</strong> n is the path-loss exp<strong>on</strong>ent for the distances greater than the break-point distance.<br />
Other c<strong>on</strong>stants are as given above.<br />
About LOS path-loss, there is a statement in [13] about trials in an rural envir<strong>on</strong>ment that show that the<br />
two-ray model woks well there. For the urban microcellular envir<strong>on</strong>ment it has been modified slightly to<br />
make it agree with the measurement results. Measurements for the two-ray modeling were <str<strong>on</strong>g>report</str<strong>on</strong>g>ed at 1.9<br />
GHz <strong>and</strong> cover the range of 0 to 1800 m with antenna heights of 6 m (BS) <strong>and</strong> 1.7 m (MS). With these<br />
values the distance of 1800 m is far bey<strong>on</strong>d the break-point distance.<br />
Also, [AlPM02] shows results for LOS c<strong>on</strong>diti<strong>on</strong>s, where the path-loss exp<strong>on</strong>ent is near 2 with st<strong>and</strong>ard<br />
deviati<strong>on</strong> of 6.9 dB. The behavior of the path loss is thus like in free-space. The envir<strong>on</strong>ment is called<br />
residential. It can be classified also suburban. Measurements were c<strong>on</strong>ducted using 100 MHz b<strong>and</strong>width.<br />
For NLOS c<strong>on</strong>diti<strong>on</strong>s, the path-loss exp<strong>on</strong>ent was 3.5 <strong>and</strong> the st<strong>and</strong>ard deviati<strong>on</strong> was 9.5 dB.<br />
In the reference [Zha02], based <strong>on</strong> measurements performed at 5.3 GHz with RF BW 30 MHz, <strong>and</strong> omnidirecti<strong>on</strong>al<br />
antennas, the path-loss <strong>models</strong>, excess delay <strong>and</strong> RMS delay-spread statistical values were<br />
obtained. In the rural envir<strong>on</strong>ments, the transmitter was placed at a hill with a mast, the total height was<br />
55 m from ground <strong>level</strong>, the height of the mobile stati<strong>on</strong> was 2.5 m <strong>on</strong> top of a car. The path-loss equati<strong>on</strong><br />
is expressed as follows:<br />
5.5.6.1.2 NLOS path-loss<br />
PL ( dB) = 21.8 + 33log<br />
10<br />
( d ) , σ = 3. 7 dB (5.49)<br />
The model has been based partly <strong>on</strong> measurements <strong>and</strong> partly <strong>on</strong> literature. There are numerous path-loss<br />
<strong>models</strong> for lower frequencies than 5 GHz, <strong>and</strong> especially for urban <strong>and</strong> suburban envir<strong>on</strong>ments. For the<br />
rural envir<strong>on</strong>ment at 5 GHz there are not many results available. One alternative is to use results of lower<br />
frequencies, e.g. 2 GHz <strong>and</strong> translate them to 5 GHz. This can be justified with results presented in the<br />
paragraph 5.5.6.1.4, which show that the path-loss properties at 2 <strong>and</strong> 6 GHz are closely related. Mean<br />
difference was found to be 8.1 dB, when the difference due to the free-space losses should be 9.7 dB.<br />
Although the results were measured in an urban envir<strong>on</strong>ment, they suggest that the rural 2 GHz path-loss<br />
model can be c<strong>on</strong>verted to 5 GHz by increasing the path loss with the difference in the free-space losses.<br />
One potential <strong>channel</strong> model for the D1 scenario is the COST-231-Hata model [26] that is c<strong>on</strong>verted for 5<br />
GHz. COST-231-Hata model for sub-urban envir<strong>on</strong>ment is<br />
d<br />
PL = ( 44.9 − 6.55log10 ( hBS<br />
))log10<br />
( ) + 45.5 + (35.46−1.1h<br />
MS<br />
)log10(<br />
f<br />
c<br />
) −13.82log10<br />
( hBS<br />
) + 0. 7hMS<br />
(5.50)<br />
1000<br />
where<br />
h BS = the height of the base stati<strong>on</strong><br />
h MS = the height of the mobile stati<strong>on</strong> (m)<br />
f c = the centre-frequency (MHz)<br />
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d = distance between BS <strong>and</strong> MS (m).<br />
The original model is applicable up to 2 GHz, <strong>and</strong> in the distance range 1 – 20 km.<br />
It should be noted that COST-231-Hata model is not a NLOS model, but it does not make difference<br />
between the propagati<strong>on</strong> c<strong>on</strong>diti<strong>on</strong>s. However, at l<strong>on</strong>ger distances the propagati<strong>on</strong> c<strong>on</strong>diti<strong>on</strong>s are mostly<br />
NLOS. So it can be applied for NLOS in spite of the afore-menti<strong>on</strong>ed fact.<br />
In reference [Zha02] the path-loss model for NLOS was<br />
PL ( dB) = −27.8<br />
+ 59log10 ( d)<br />
, σ =1. 9 dB (5.51)<br />
The parameters differ quite much from the values found out in this campaign. One reas<strong>on</strong> is the hilly<br />
terrain, the other could be the relatively small number of routes measured.<br />
One interesting empirical <strong>channel</strong> model for suburban envir<strong>on</strong>ment is [Erc99]. The suburban envir<strong>on</strong>ment<br />
is divided to three sub-envir<strong>on</strong>ments according to the tree density <strong>and</strong> the height variati<strong>on</strong> of the<br />
envir<strong>on</strong>ment. One of these envir<strong>on</strong>ments could well be applied to the rural envir<strong>on</strong>ment. This model is<br />
created for 1. 9 GHz. With the same reas<strong>on</strong>ing as afore it can be also extended to 5 GHz. The model is<br />
PL = 20 log10 (4p 100/?) + 10 (a – b hBS + c/hBS) log10 (d/100) (5.52)<br />
where the parameters a, b <strong>and</strong> c may get three sets of values depending <strong>on</strong> the envir<strong>on</strong>ment (see below)<br />
<strong>and</strong> the other parameters are the same as in the previous formula. The model is applicable for distances<br />
100 m – 20 km.<br />
The parameter set that is closest to the rural envir<strong>on</strong>ment in Tyrnävä is the <strong>on</strong>e for low tree density <strong>and</strong><br />
flat terrain. Then the c<strong>on</strong>stants are: a = 3.6, b = 0.005 <strong>and</strong> c = 20. The complete model defines also a<br />
distance dependent st<strong>and</strong>ard deviati<strong>on</strong> for the path loss, but it is not discussed further in this document.<br />
5.5.6.1.3 Probability of LOS<br />
There are few references about the LOS probability. Especially for the rural envir<strong>on</strong>ment where relatively<br />
high BS antenna heights are likely to be used. Reference [30] discusses LOS probability in a peer to peer<br />
<strong>and</strong> ad-hoc envir<strong>on</strong>ment, where the antenna heights are low. The result is that the LOS probability<br />
decreases from <strong>on</strong>e to zero approximately in the interval 30 m to 300 m. No formula for the decay is<br />
given.<br />
In [SCM] there is a model given for the LOS probability. The probability formula proposed is<br />
p LOS (d) = (d 0 - d) / d 0 , 0 < d
WINNER D5.4 v. 1.4<br />
5.6 Interpretati<strong>on</strong> of results<br />
5.6.1 Path-loss<br />
5.6.1.1 Scenario A1<br />
5.6.1.1.1 Proposed path-loss model<br />
The results for path loss <strong>and</strong> shadowing have been summarized in Table 5.45.<br />
Table 5.45: Path-loss <strong>and</strong> shadowing characteristics in the indoor envir<strong>on</strong>ment.<br />
Indoor LOS (c-c) NLOS (r–c)<br />
PL at 5.25 GHz<br />
SF st<strong>and</strong>ard deviati<strong>on</strong> at<br />
5.25 GHz<br />
46.8 +18.7 log10(d),<br />
d >1m<br />
s = 3.1 dB<br />
PL (d)= 38.8+36.8 log10(d)<br />
d >5m<br />
s = 3.5 dB<br />
5.6.1.1.2 Probability of LOS<br />
The probability of line-of-sight (LOS) propagati<strong>on</strong> vs. distance is a functi<strong>on</strong> we denote the pLOS<br />
functi<strong>on</strong>. For scenario A1, this characteristic can be derived analytically because the geometry of the<br />
scenario is known exactly.<br />
A simple ad-hoc fit of the derived pLOS functi<strong>on</strong> is given as:<br />
p LOS (d) = 1 – (1 – x 3 ) 1/3 * (1 – 5 / 50), (5.55)<br />
where x = 1 - log 10 (d / 2.5) / log 10 (100 / 2.5).<br />
5.6.1.2 Scenario B5a<br />
We use the path-loss model of [PT00] as given below. We assume that it is applicable from 30 meters to<br />
2km distance with a correcti<strong>on</strong> term for frequency, i.e.<br />
( fc<br />
/ 2.5GHz) + 23.5log10( d + δ<br />
slow<br />
Loss = 36 .5 + 20log10<br />
) , 300 m < d < 8 km (5.56)<br />
We note that for the 30m to 300m range (for which [PT00] presents no measurements), the path-loss<br />
model almost coincides with cases in [Dug99] with the smallest path-loss. These cases are probably the<br />
<strong>on</strong>es with the least obstructed LOS. The model of [PT00] is for 2.5GHz. For other centre-frequencies, fc,<br />
it seems reas<strong>on</strong>able to translate by using the free-space frequency dependence as the propagati<strong>on</strong> scenario<br />
(e.g. path-loss exp<strong>on</strong>ent) is close to free-space propagati<strong>on</strong>.<br />
The shadow fading is Gaussian with mean zero <strong>and</strong> st<strong>and</strong>ard deviati<strong>on</strong> σ SF = 3.4 dB based <strong>on</strong> [PT00].<br />
5.6.1.3 Scenario B5b<br />
Based <strong>on</strong> the observati<strong>on</strong> of numerous papers that the path loss follows approximately a free-space law<br />
before the breakpoint distance we will assume that loss is given by<br />
Loss<br />
( r) = −20log( /( 4πr)<br />
) + σ free + δ free,<br />
r ≤ rb<br />
λ , d < 1 km (5.57)<br />
where the first part is recognized as the free-space path-loss, d free is a Gaussian distributed r<strong>and</strong>om<br />
variable (shadow fading), with st<strong>and</strong>ard deviati<strong>on</strong> s free = 3 dB. This path-loss model (i.e., (4.10)) can be<br />
used for a maximum distance of 1 km. Many measurements seem to show path loss lower than the freespace<br />
before the breakpoint <strong>and</strong> indeed it can happen due to c<strong>on</strong>structive multi-path. However, to avoid<br />
producing overly optimistic results the extra loss s free has been introduced such that the probability of a<br />
lower than free-space loss is <strong>on</strong>ly some 14%. The breakpoint distance is calculated as<br />
( h − h )( h − h )<br />
b 0 b 0<br />
rb = 4<br />
(5.58)<br />
λ<br />
where we, somewhat pessimistically, set the effective ground <strong>level</strong> h0<br />
to 1.6 meter. For distances larger<br />
than r b we set the path loss to<br />
Loss<br />
( r) = free − 20log10( λ /( 4πrb<br />
)) + 40log( r / rb ) + δ bey<strong>on</strong>d,<br />
r > rb<br />
σ , (5.59)<br />
where the first two teRMS corresp<strong>on</strong>d to the (mean) path-loss at b<br />
r <strong>and</strong> d bey<strong>on</strong>d is a Gaussian shadowfading<br />
term with mean zero <strong>and</strong> st<strong>and</strong>ard deviati<strong>on</strong> 7 dB.<br />
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5.6.1.4 Scenario C1<br />
5.6.1.4.1 LOS propagati<strong>on</strong> c<strong>on</strong>diti<strong>on</strong>s<br />
The measured path-loss formula is<br />
where d is the distance.<br />
PL(d) = 41.6 + 23.8 log 10 (d), s = 4 dB (5.60)<br />
The measurement range was limited to 600 m. However, like e.g. in the rural scenario, it is assumed that<br />
the range can be extended to the break-point distance, because the LOS propagati<strong>on</strong> is not very sensitive<br />
to the envir<strong>on</strong>ment. As well the frequency range can be extended to other frequencies. The path-loss<br />
formula can be expressed as<br />
where d = distance<br />
PL(d) = 41.6 + 23.8 log 10 (d), s = 4.0 dB, 20 m < d d BP<br />
The formula above can be adapted for the frequencies between 2000 <strong>and</strong> 6000 MHz by replacing the<br />
c<strong>on</strong>stant 41.6 by a factor<br />
5.6.1.5 Scenario D1<br />
5.6.1.5.1 LOS model<br />
C(f c ) = 33.2 + 20 log10(f c /(2·10 9 )) (5.62)<br />
The path loss is shown in the figure below for the two centre-frequencies 2.45 <strong>and</strong> 5.25 GHz in LOS<br />
propagati<strong>on</strong> c<strong>on</strong>diti<strong>on</strong>s. The measurements have been c<strong>on</strong>ducted in a 100 MHz b<strong>and</strong>width. The curve for<br />
2.45 GHz c<strong>on</strong>tains also a part that is measured in NLOS (or nearly NLOS) c<strong>on</strong>diti<strong>on</strong>s around 1000 m<br />
distance.<br />
130<br />
path loss (dB)<br />
120<br />
110<br />
100<br />
90<br />
80<br />
70<br />
PL(d) = -105 + 75.0*log10(d), σ = 2.2 dB<br />
PL(d) = 41.8 + 22.0*log10(d), σ = 2.6 dB<br />
5.25 GHz<br />
Free space<br />
2.45 GHz<br />
PL(d) = 38.3 + 21.1*log10(d), σ = 2.9 dB<br />
100 200 500 1000<br />
distance from MS to BS (m)<br />
Figure 5.88: Rural path-loss at 2.45 <strong>and</strong> 5.25 GHz.<br />
The measured model for LOS has been extended from the [D5.3] for l<strong>on</strong>ger ranges, because it has<br />
become evident that a path-loss model for LOS c<strong>on</strong>diti<strong>on</strong>s with l<strong>on</strong>ger BS – MS distances is needed. The<br />
maximum distance for the model should be 10 km. Theoretically the model shown in (5.63) can be<br />
extended until so called break-point distance, which depends <strong>on</strong> the wave-length ? <strong>and</strong> base stati<strong>on</strong> <strong>and</strong><br />
mobile stati<strong>on</strong> antenna heights h BS <strong>and</strong> h MS , respectively. After this break-point the loss is proporti<strong>on</strong>al to<br />
another, greater path-loss exp<strong>on</strong>ent. By flat earth theory this exp<strong>on</strong>ent should be 4, but in practice it can<br />
be also smaller or greater. In practice the break point distance varies around the theoretical value. The<br />
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break-points or the dual-slope behaviour could not be found in our measurements. However, this depends<br />
most probably <strong>on</strong> the r<strong>and</strong>omness of the practical situati<strong>on</strong>: We have decided to take it as part of our rural<br />
LOS model.<br />
For the line of sight (LOS) c<strong>on</strong>diti<strong>on</strong>s the measurements suggest the path-loss equati<strong>on</strong>s:<br />
with the st<strong>and</strong>ard deviati<strong>on</strong> s = 3.5 dB.<br />
PL(d) = 44.6 + 21.5 log 10 (d) (5.63)<br />
The model is based partly <strong>on</strong> our measurements <strong>and</strong> partly <strong>on</strong> the literature research, where we have<br />
adopted the two ray model for distances higher than the break-point. For the LOS envir<strong>on</strong>ment we get<br />
then:<br />
where d = distance<br />
PL(d) = 44.6 + 21.5 log 10 (d), s = 3.5 dB, d d BP<br />
The path losses behave very similarly at the two frequencies. As a matter of fact the mean behaviour is<br />
very near free-space path-loss in LOS c<strong>on</strong>diti<strong>on</strong>s. The formula above can be adapted for the frequencies<br />
between 2000 <strong>and</strong> 6000 MHz by replacing the c<strong>on</strong>stant 44.6 by a factor<br />
C(f c ) = 36.2 + 20 log 10 (f c /(2·10 9 )) (5.65)<br />
Note that the LOS path-loss depends <strong>on</strong> antenna heights <strong>on</strong>ly through the break-point distance.<br />
5.6.1.5.2 NLOS model<br />
The NLOS model is based <strong>on</strong> our measurements which have been fitted to results found in the literature<br />
research. The measured path-loss curve for the NLOS c<strong>on</strong>diti<strong>on</strong>s has the equati<strong>on</strong><br />
with s = 6.7 dB.<br />
PL = 55.8 + 25.1 log 10 (d) (5.66)<br />
This path-loss equati<strong>on</strong> was measured for the BS antenna height 23 m <strong>and</strong> MS antenna height 1.7 m. This<br />
equati<strong>on</strong> will be compared to some theoretical path-loss curves. From the literature we found that mostly<br />
the st<strong>and</strong>ard deviati<strong>on</strong> was a little bit higher than 6.7. Because our measurement was limited, we decided<br />
to use the value 8 dB for the st<strong>and</strong>ard deviati<strong>on</strong>.<br />
The formula (5.66) above can be adapted for the frequencies between 2000 <strong>and</strong> 6000 MHz by replacing<br />
the c<strong>on</strong>stant 55.8 by a factor<br />
C(f c ) = 46.9 + 20 log 10 (f c / (2·10 9 )) · 1.063 (5.67)<br />
= 46.9 + 21.3 log 10 (f c / (2·10 9 ))<br />
The c<strong>on</strong>stant 1.063 is based <strong>on</strong> our finding that the path loss difference in between 5.25 <strong>and</strong> 2.45 GHz for<br />
NLOS c<strong>on</strong>diti<strong>on</strong>s was about 2.5 dB higher than for free space, see paragraph 5.6.9.<br />
After D5.3 the following equati<strong>on</strong> was proposed for the D1 rural NLOS scenario path-loss by the WP5 to<br />
other work-packages. This model is called. COST231-Hata model, originally for urban <strong>and</strong> suburban<br />
envir<strong>on</strong>ments. Slightly simplified it reads as<br />
f<br />
c<br />
d<br />
PL = 20log10 ( ) + [ 44.9 −6.55log10<br />
( hBS<br />
) ] log10<br />
( ) −13.82log10<br />
( hBS<br />
) + 153. 39 (5.68)<br />
2000<br />
1000<br />
where h BS is the height of the base stati<strong>on</strong>, f c is the centre-frequency (MHz), <strong>and</strong> d is the distance between<br />
BS <strong>and</strong> MS (m).<br />
This model <strong>and</strong> another well-known model, Erceg model 1, will be compared to the measured curve.<br />
The modificati<strong>on</strong> of the BS antenna height is probably needed, because the envir<strong>on</strong>ment of the<br />
measurements is extremely flat. This can be compensated by adding 25 m to get an effective BS antenna<br />
height that is greater in flat than in hilly envir<strong>on</strong>ments.<br />
Both <strong>models</strong> work equally well, if the BS antenna height is between, say, 10 <strong>and</strong> 100 m. For higher<br />
antenna heights the curves begin to differ. According to the comparis<strong>on</strong>, the modified COST231-Hata<br />
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model can be used from 100 m to 10 km, although originally the model has been defined for distances<br />
greater than 1 km. The modified Erceg model 1 could be applied as well.<br />
5.6.1.5.3 Unified model<br />
For the overall path-loss in the D1 scenario we get from measurements the formula<br />
PL(d) = 50.4 + 25.8 log 10 (d) (5.69)<br />
The drawback of this model is that it is based <strong>on</strong> relatively few measurements. In additi<strong>on</strong> the LOS<br />
c<strong>on</strong>diti<strong>on</strong> disappeared after quite a small distance from the BS. This depends e.g. <strong>on</strong> the fact that the BSs<br />
were located slightly off the roads for practical reas<strong>on</strong>s. Real BS:s would be located probably in more<br />
beneficial way.<br />
The unified model can be formed also by combining the LOS model <strong>and</strong> the NLOS model using the LOS<br />
probability p LOS (d):<br />
PL(d) = p LOS (d) PL LOS (d) + [1- p LOS (d)] PL NLOS (d) (5.70)<br />
where d is the distance between MS <strong>and</strong> BS. p LOS will be defined in Secti<strong>on</strong> 5.6.1.5.4<br />
The drawback of this model is that the p LOS used here is <strong>on</strong>ly theoretical <strong>on</strong>e. However, it gives<br />
reas<strong>on</strong>able results, <strong>and</strong> is used therefore as basis of comparis<strong>on</strong> of our model <strong>and</strong> the <strong>models</strong> found in the<br />
literature.<br />
5.6.1.5.4 Probability of LOS model<br />
Probability of LOS in the D1 scenario is proposed to be modelled with an exp<strong>on</strong>ential functi<strong>on</strong><br />
P LOS<br />
d<br />
( d)<br />
= exp( − )<br />
(5.71)<br />
where d is the distance between the BS <strong>and</strong> the MS <strong>and</strong> d 0 is the c<strong>on</strong>stant defining the steepness of the<br />
exp<strong>on</strong>ential decay.<br />
Default value for d 0<br />
is proposed to be 1 km. The reas<strong>on</strong> for proposing this model is the following: It is<br />
very near the model for LOS probability defined in [SCM] at small distances. In additi<strong>on</strong> it does not go to<br />
zero at the cell boundary, so that it can be used in the <strong>system</strong>-<strong>level</strong> modelling of interference.<br />
5.6.1.5.5 Model comparis<strong>on</strong><br />
In the figure below there are the measurement based PL curves obtained in the WINNER project<br />
compared to some well-known PL <strong>models</strong> from the literature. In additi<strong>on</strong> there is the free-space loss<br />
curve. In the comparis<strong>on</strong> the base stati<strong>on</strong> antenna height was 24.5 m; mobile antenna height was the<br />
default value 1.7 m.<br />
d 0<br />
Figure 5.89: Comparis<strong>on</strong> of <strong>channel</strong> <strong>models</strong> for a rural envir<strong>on</strong>ment.<br />
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When adjusting the PL curves of the <strong>models</strong> to closely follow the measured over-all curve, the following<br />
acti<strong>on</strong>s were needed:<br />
1. Cost231-Hata: Subtract 15 dB <strong>and</strong> apply h BS = 50 m (instead of the 25 m). I.e. use an effective<br />
BS antenna height h BS + 25 m.<br />
2. Erceg: Apply h BS = 60 m. I.e. use an effective BS antenna height h BS + 35 m.<br />
Both <strong>models</strong> could be used, after these modificati<strong>on</strong>s. The subtracti<strong>on</strong> of 15 dB needed with the<br />
COST231-Hata model is caused by the fact that the model is not originally planned for rural<br />
envir<strong>on</strong>ments, but for urban <strong>and</strong> suburban envir<strong>on</strong>ments.<br />
5.6.2 Power-delay profile<br />
5.6.2.1 Scenario A1<br />
The PDP at a corridor to corridor envir<strong>on</strong>ment is modelled as a decaying exp<strong>on</strong>ential. The measured PDP<br />
has a spike that can be identified coming due to a reflecti<strong>on</strong> from the end of the corridor, see Secti<strong>on</strong><br />
5.4.7.1. In our model we have neglected it, because the delay of the spike depends <strong>on</strong> the locati<strong>on</strong> of the<br />
BS in the corridor. The c<strong>on</strong>stants of the decay have been determined in the same paragraph. It is relatively<br />
easy to extend the model to include the spike, when the locati<strong>on</strong> of the BS is fixed. However, it is not<br />
included in the current model. This is justified by the model simplicity <strong>and</strong> also the relative low <strong>level</strong> of<br />
the measured spike. For the corridor to room envir<strong>on</strong>ment the model fits quite well in the exp<strong>on</strong>ential<br />
model.<br />
5.6.2.2 Scenario B5a<br />
The power-delay profile (of all paths except the direct) is set as exp<strong>on</strong>ential, based <strong>on</strong> the results in<br />
[OBL+02] <strong>and</strong> [SCK05].<br />
5.6.2.3 Scenario B5b<br />
The power-delay profile (of all paths except the direct) is set as exp<strong>on</strong>ential, based <strong>on</strong> the results in<br />
[SMI+00]. A per-path shadow fading of 3 dB is used to obtain some variati<strong>on</strong> in the impulse resp<strong>on</strong>ses.<br />
5.6.3 Delay spread<br />
5.6.3.1 Scenario B5a<br />
The RMS-delay-spread is set to 40 ns, based <strong>on</strong> [PT00]. In order to have a valid model, it requires beamwidths<br />
comparable to those employed in [PT00].<br />
5.6.3.2 Scenario B5b<br />
Based <strong>on</strong> the delay-spread formula in [MAS02] i.e.<br />
s<br />
[ ns] exp( β )<br />
= (5.72)<br />
we select the delay spread to be 30 ns when the path loss is less than 85 dB, 110 ns when the path loss is<br />
between 85 dB <strong>and</strong> 110 dB, <strong>and</strong> finally 380 ns when the path loss is greater than 110 dB. With these<br />
settings the delay-spread used here is a factor 40%-156% of the delay-spread formula of [MAS02] for<br />
path losses up to 137 dB. We call these path-loss intervals range1, range2 <strong>and</strong> range3 <strong>and</strong> different<br />
clustered-delay line <strong>models</strong> will be provided for the three cases.<br />
5.6.3.3 Scenario C1<br />
In the C1 LOS suburban scenario we model the PDP with a single exp<strong>on</strong>ential. It can be seen that another<br />
exp<strong>on</strong>ential cluster could be included in the PDP. For the same reas<strong>on</strong> as in the scenario A1 it was<br />
decided to be neglected.<br />
5.6.3.4 Scenario D1<br />
In the D1 scenario the PDP is best modelled with two decaying exp<strong>on</strong>entials in both LOS <strong>and</strong> NLOS<br />
propagati<strong>on</strong> c<strong>on</strong>diti<strong>on</strong>s, i.e. with a dual-slope model. However, also now we model the PDP was decided<br />
to be modelled with a single exp<strong>on</strong>ential in both cases. In the LOS c<strong>on</strong>diti<strong>on</strong>s the first part is modelled<br />
with <strong>on</strong>e spike <strong>and</strong> the sec<strong>on</strong>d part with an exp<strong>on</strong>ential.<br />
In the NLOS c<strong>on</strong>diti<strong>on</strong>s a single slope model is fitted to the measured PDP for simplicity. The fitting is<br />
performed to preserve the modelled RMS-delay spread equal to the measured <strong>on</strong>e.<br />
PL dB<br />
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5.6.4 K-factor<br />
5.6.4.1 Scenario B5a<br />
A static (n<strong>on</strong>-fading) <strong>channel</strong> comp<strong>on</strong>ent is added to the impulse resp<strong>on</strong>se. We select this parameter to be<br />
10 dB. This is based <strong>on</strong> the worst case (smallest value) in [SCK05]. In [OBL+02] a somewhat smaller<br />
average of 2.3 dB is seen but this is probably due to the LOS obstructi<strong>on</strong>s by trees.<br />
5.6.4.2 Scenario B5b<br />
A static (n<strong>on</strong>-fading) <strong>channel</strong> comp<strong>on</strong>ent is added to the impulse resp<strong>on</strong>se. Based <strong>on</strong> [FDS+94] we select<br />
this parameter to be 10 in range1, 2 in range2, <strong>and</strong> 1 in range3 (for a definiti<strong>on</strong> of the ranges see the<br />
secti<strong>on</strong> <strong>on</strong> delay-spread above.)<br />
5.6.5 Cross-polarizati<strong>on</strong> discriminati<strong>on</strong> (XPR)<br />
5.6.5.1 Scenario B5a<br />
The polarizati<strong>on</strong> scrambling (i.e. the power transfer between a transmitted vertically polarized to a<br />
received horiz<strong>on</strong>tally polarized antenna, <strong>and</strong> vice versa) is highly related to reflecti<strong>on</strong> <strong>on</strong> rough surfaces.<br />
This effect should be small in LOS scenarios. A high XPR means that there is little power transfer<br />
between the comp<strong>on</strong>ents. This means that we should be able to use the highest XPR values measured in<br />
[Dug99]. However, in order to avoid overly optimistic results we chose the mean value of [Dug99] i.e.<br />
30dB.<br />
5.6.5.2 Scenario B5b<br />
Based <strong>on</strong> the results in [KVV05] we set the XPR to 9 dB.<br />
5.6.6 Doppler<br />
5.6.6.1 Scenario B5a<br />
The Doppler is modelled by moving the scatterers appropriately. We chose the spectrum of [DGM+03]<br />
since it is assumed to be the most similar to the applicati<strong>on</strong> here.<br />
5.6.6.2 Scenario B5b<br />
We propose the introducti<strong>on</strong> of individual Doppler frequencies similar to the model in [TPE02]. We<br />
select the Doppler model [Erc01] which has somewhat larger Doppler spread than [DGM+03] probably<br />
due to the influence of traffic.<br />
5.6.7 Angle-spread<br />
5.6.7.1 Scenario B5a<br />
Based <strong>on</strong> our visual inspecti<strong>on</strong> of the plots in [SCK05] we set the AoD <strong>and</strong> AoA of the n<strong>on</strong>-direct paths<br />
to be Gaussian with composite power weighted angle-spread of 2 degrees. The ZDSC angle-spread is set<br />
to 0.5 degree.<br />
5.6.7.2 Scenario B5b<br />
Based <strong>on</strong> our visual inspecti<strong>on</strong> of the plots in [MIS01] we set the AoD of all the paths to be uniformly<br />
distributed between 0 <strong>and</strong> 360 degrees. The direct path is aligned with the geometrical angle between the<br />
transmitter <strong>and</strong> receiver. The intra-cluster angle-spread is set to 2 degrees.<br />
5.6.8 Antenna gain<br />
5.6.8.1 Scenario B5<br />
The antenna pattern that can be used in the simulati<strong>on</strong> is specified by<br />
A<br />
( γ )<br />
⎡ ⎛ γ ⎞<br />
=−min⎢12<br />
⎜ ⎟<br />
⎢ ⎝γ<br />
3dB<br />
⎣ ⎠<br />
2<br />
A<br />
,<br />
m<br />
⎤<br />
o<br />
o<br />
⎥, where 180 < φ
WINNER D5.4 v. 1.4<br />
5.6.9 Frequency dependence of the propagati<strong>on</strong> parameters<br />
5.6.9.1 Path-loss <strong>and</strong> shadowing properties<br />
In the Figure 5.90, the path losses at 5.25 GHz <strong>and</strong> 2.45 GHz are compared in a rural LOS envir<strong>on</strong>ment in<br />
the same route. It is obvious that the path-loss functi<strong>on</strong>s can be modeled so that the <strong>on</strong>ly the difference<br />
between them is the difference between the free-space path-losses, i.e. 6.62 dB.<br />
Figure 5.90: Propagati<strong>on</strong> at 2.45 GHz <strong>and</strong> 5.25 GHz in rural LOS c<strong>on</strong>diti<strong>on</strong>s.<br />
In the figure Figure 5.91, the path losses at 2.45 GHz (a) <strong>and</strong> 5.25 GHz (b) are compared in a rural<br />
LOS/NLOS envir<strong>on</strong>ment in the same route. The over-all behaviour of the path loss is almost identical.<br />
a<br />
Figure 5.91: Path-loss at 2.45 GHz (a) <strong>and</strong> 5.25 GHz (b) measured in the same route.<br />
b<br />
It can be seen that the path losses are almost identical except for the difference due to the free-space<br />
losses. However, we have calculated that there is a difference of 1.7 dB in the overall st<strong>and</strong>ard deviati<strong>on</strong><br />
<strong>and</strong> a difference of 1 dB in the NLOS st<strong>and</strong>ard deviati<strong>on</strong> of the path losses between the centre-frequencies<br />
5.25 GHz <strong>and</strong> 2.45 GHz. From this we c<strong>on</strong>clude that that the propagati<strong>on</strong> is attenuated more in 5 GHz<br />
than in 2 GHz due to shadowing. We assume that the higher st<strong>and</strong>ard deviati<strong>on</strong> is caused by the fact that<br />
the obstacles attenuate more at 5.25 Hz than in 2.45 GHz so that the fades caused by the shadowing are<br />
deeper for the 5.25 GHz. From the measurements we can calculate that the extra loss is about 2 - 3 dB.<br />
For the model we decided to select the value 2.5 dB. For LOS/OLOS c<strong>on</strong>diti<strong>on</strong>s we could find a similar<br />
result, but the difference was negligible.<br />
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When comparing the shadow fading autocorrelati<strong>on</strong>, we got the following results for the autocorrelati<strong>on</strong>s<br />
of the over-all shadowing: For 2.45 GHz the correlati<strong>on</strong> distance was approximately 330 m <strong>and</strong> for the<br />
5.25 GHz it was approximately 320 m. Note that the route used for the comparis<strong>on</strong> was the <strong>on</strong>e with the<br />
greatest correlati<strong>on</strong> distance.<br />
5.6.9.2 Rms-delay spread<br />
The RMS delay spread is shown in the Table 5.46 for the centre-frequencies 2.45 <strong>and</strong> 5.25 GHz. The<br />
difference is c<strong>on</strong>siderable. For 2.45 GHz the mean delay spread for LOS is 35 % <strong>and</strong> for NLOS 80 %<br />
higher. Same trend can be found in the references cited in Secti<strong>on</strong> 5.5.<br />
Table 5.46: Rms-delay spread percentiles at 2.45 <strong>and</strong> 5.25 GHz.<br />
Rms delay spread<br />
(ns)<br />
LOS<br />
NLOS<br />
2 GHz 5 GHz 2 GHz 5 GHz<br />
10% 6.4 2.5 12.3 4.3<br />
50% 22.7 15.4 61.0 37.1<br />
90% 64.0 84.4 130.0 89.5<br />
mean 30.2 36.8 69.0 42.1<br />
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6. Channel Model Implementati<strong>on</strong><br />
The purpose of this chapter is to discuss issues c<strong>on</strong>cerning implementati<strong>on</strong> of the WINNER <strong>channel</strong><br />
model.<br />
6.1 Overview for implementing the model<br />
WINNER <strong>channel</strong> model needs as an input the general informati<strong>on</strong> like <strong>channel</strong> scenario <strong>and</strong> MIMO<br />
setup, antenna c<strong>on</strong>figurati<strong>on</strong>s like radiati<strong>on</strong> patterns <strong>and</strong> array geometries <strong>and</strong> <strong>system</strong> layout informati<strong>on</strong><br />
like relative distances <strong>and</strong> orientati<strong>on</strong>s of the transceivers. Output of the model is a set of discrete <strong>channel</strong><br />
impulse resp<strong>on</strong>ses with matrix coefficients (see eq 3.26). Entries of the matrices are complex <strong>channel</strong><br />
coefficients for each transmitter receiver antenna element pairs. Channel impulse resp<strong>on</strong>ses are<br />
realisati<strong>on</strong>s of the radio <strong>channel</strong> for discrete time instants <strong>and</strong> for different radio <strong>link</strong>s.<br />
6.1.1 Time sampling <strong>and</strong> interpolati<strong>on</strong><br />
Channel sampling frequency has to be finally equal to the simulati<strong>on</strong> <strong>system</strong> sampling frequency. To have<br />
feasible computati<strong>on</strong>al complexity it is not possible to generate <strong>channel</strong> realisati<strong>on</strong>s <strong>on</strong> the sampling<br />
frequency of the <strong>system</strong> to be simulated. The <strong>channel</strong> realisati<strong>on</strong>s have to be generated <strong>on</strong> some lower<br />
sampling frequency <strong>and</strong> then interpolated to the desired frequency. A practical soluti<strong>on</strong> is e.g. to generate<br />
<strong>channel</strong> samples with sample density (over-sampling factor) two, interpolate them accurately to sample<br />
density 64 <strong>and</strong> to apply zero order hold interpolati<strong>on</strong> to the <strong>system</strong> sampling frequency. Channel impulse<br />
resp<strong>on</strong>ses can be generated during the simulati<strong>on</strong> or stored <strong>on</strong> a file before the simulati<strong>on</strong> <strong>on</strong> low sample<br />
density. Interpolati<strong>on</strong> can be d<strong>on</strong>e during the <strong>system</strong> simulati<strong>on</strong>.<br />
6.1.2 Coordinate <strong>system</strong><br />
System layout in the Cartesian coordinates is for example the following:<br />
Figure 6.1: System layout of multiple base stati<strong>on</strong>s <strong>and</strong> mobile stati<strong>on</strong>s.<br />
All the BS <strong>and</strong> MS have (x,y) coordinates. MS <strong>and</strong> cells (sectors) have also array broad side orientati<strong>on</strong>,<br />
where north (up) is the zero angle. Positive directi<strong>on</strong> of the angles is the clockwise directi<strong>on</strong>.<br />
Table 6.1: Transceiver coordinates <strong>and</strong> orientati<strong>on</strong>s.<br />
Tranceiver Co-ordinates Orientati<strong>on</strong> [°]<br />
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BS1 cell1 (x bs1 ,y bs1 ) Ω c1<br />
cell2 (x bs1 ,y bs1 ) Ω c2<br />
cell3 (x bs1 ,y bs1 ) Ω c3<br />
BS2 cell4 (x bs2 ,y bs2 ) Ω c4<br />
cell5 (x bs2 ,y bs2 ) Ω c5<br />
cell6 (x bs2 ,y bs2 ) Ω c6<br />
MS1 (x ms1 ,y ms1 ) Ω ms1<br />
MS2 (x ms2 ,y ms2 ) Ω ms2<br />
MS3 (x ms3 ,y ms3 ) Ω ms3<br />
Both the distance <strong>and</strong> line of sight (LOS) directi<strong>on</strong> informati<strong>on</strong> of the radio <strong>link</strong>s are calculated for the<br />
input of the model. Distance between the BS i <strong>and</strong> MS k is<br />
d<br />
( 2<br />
2<br />
BS ,<br />
) ( )<br />
i MS<br />
x<br />
k BS<br />
− x<br />
i MS<br />
+ y<br />
k BS<br />
− y<br />
i MS k<br />
= . (6.1)<br />
The LOS directi<strong>on</strong> from BS i to MS k with respect to BS antenna array broad side is (see Figure 6.2)<br />
⎧ ⎛ y ⎞<br />
MS<br />
− y<br />
⎪ ⎜<br />
k BSi<br />
− arctan<br />
⎟ + 90° − Ω<br />
BS<br />
, when x ≥<br />
⎪<br />
i<br />
MS<br />
x<br />
k BSi<br />
⎝<br />
xMS<br />
− x<br />
k BSi<br />
⎠<br />
θ<br />
BS<br />
= ⎨<br />
i , MS<br />
(6.2)<br />
k<br />
⎪ ⎛ yMS<br />
− y ⎞<br />
⎜<br />
k BSi<br />
⎟<br />
⎪−<br />
arctan<br />
− 90° − Ω<br />
BS<br />
, when x <<br />
i<br />
MS<br />
x<br />
k BSi<br />
⎩ ⎝<br />
xMS<br />
− x<br />
k BSi<br />
⎠<br />
The angles <strong>and</strong> orientati<strong>on</strong>s are depicted in the figure below.<br />
Ω BSi<br />
θ BS i , MS k<br />
Ω MSk<br />
θ MS , k BS i<br />
Figure 6.2: BS <strong>and</strong> MS antenna array orientati<strong>on</strong>s.<br />
Pairing matrix A is in the example case of Figure 6.2 a 3x6 matrix with values {0,1}. Value 0 st<strong>and</strong>s for<br />
<strong>link</strong> MSm to celln is not modelled <strong>and</strong> value 1 for <strong>link</strong> is modelled.<br />
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⎡χ<br />
ms1,<br />
c1<br />
χ<br />
ms1,<br />
c2<br />
L χ<br />
ms1,<br />
c6<br />
⎤<br />
⎢<br />
⎥<br />
= ⎢<br />
χ<br />
ms2,<br />
c1<br />
χ<br />
ms1,<br />
c2<br />
L χ<br />
ms1,<br />
c6<br />
A ⎥<br />
(6.3)<br />
⎢ M M O M ⎥<br />
⎢<br />
⎥<br />
⎣χ<br />
ms3,<br />
c1<br />
χ<br />
ms3,<br />
c2<br />
L χ<br />
ms3,<br />
c6<br />
⎦<br />
The pairing matrix can be applied to select which radio <strong>link</strong>s will be generated <strong>and</strong> which will not.<br />
6.1.3 Generati<strong>on</strong> of correlated large-scale parameters<br />
The <strong>system</strong> <strong>level</strong> modelling will introduce some locati<strong>on</strong> dependency between the radio <strong>link</strong>s. This is<br />
d<strong>on</strong>e by correlated large-scale <strong>channel</strong> parameters for the radio <strong>link</strong>s. There can be identified five<br />
different cases in the correlati<strong>on</strong> point of view:<br />
1. One MS is c<strong>on</strong>nected to two different BS<br />
2. One MS is c<strong>on</strong>nected to two different sectors of a single BS<br />
3. Two different MSs are c<strong>on</strong>nected to <strong>on</strong>e sector of a BS<br />
4. Two different MSs are c<strong>on</strong>nected to two different sectors of a single BS<br />
5. Two different MSs are c<strong>on</strong>nected to two different BSs<br />
The radio <strong>link</strong>s in the cases 1 <strong>and</strong> 5 are n<strong>on</strong> correlated, case 2 is fully correlated <strong>and</strong> in the cases 3 <strong>and</strong> 4<br />
the correlati<strong>on</strong> is a functi<strong>on</strong> of distance between MSs. Excepti<strong>on</strong> is the shadow fading, which is correlated<br />
also in case 1 with a fixed factor.<br />
Currently, the following large-scale parameters to be correlated are:<br />
1. Delay-spread (DES)<br />
2. AoD angle-spread (ASD)<br />
3. AoA angle-spread (ASA)<br />
4. Shadow fading (SHF)<br />
5. AoD elevati<strong>on</strong> spread (ESD)<br />
6. AoA elevati<strong>on</strong> spread (ESA)<br />
? (all of which have<br />
mean zero <strong>and</strong> variance <strong>on</strong>e) in the positi<strong>on</strong>s x<br />
i<br />
, yi<br />
where the mobiles are located. The elements of<br />
?( x, y)<br />
are uncorrelated, see Secti<strong>on</strong> 4.1.4.2. However the auto-correlati<strong>on</strong> of is n<strong>on</strong>-zero. More prisecely<br />
the correlati<strong>on</strong> between element c of the ?( x, y)<br />
vector, i.e. ?<br />
c<br />
( x,<br />
y)<br />
, in two points x , y 1 1 <strong>and</strong> x , y 2 2 is<br />
given by<br />
The first step is to generate the vector of four real-valued Gaussian variables ( x, y)<br />
E<br />
⎛<br />
2<br />
2 ⎞<br />
⎜ ( x1<br />
− x2)<br />
+ ( y1<br />
− y2)<br />
( = −<br />
⎟<br />
1 1 c 2 2<br />
exp<br />
(6.4)<br />
⎜<br />
λ ⎟<br />
⎝<br />
c<br />
⎠<br />
{ ξ x , y ) ξ ( x , y )}<br />
c<br />
To obtain these values for the K <strong>link</strong>s between a base-stati<strong>on</strong> <strong>and</strong> K users we may start by defining a<br />
correlati<strong>on</strong> matrix C of size KxK <strong>and</strong> then for the square root of this matrix as C = MM T <strong>and</strong> then obtain<br />
the samples as<br />
where = [ ξc<br />
( x<br />
1, y1) , K,<br />
ξc<br />
( xK<br />
, yK<br />
)]<br />
with mean zero <strong>and</strong> variance <strong>on</strong>e. Alternatively, ( x y)<br />
G = Mn , (6.5)<br />
G <strong>and</strong> n is Kx1 vector of independent real-valued Gaussian variables<br />
?<br />
c<br />
, can be generated for a grid of points by first<br />
generating a grid of independent samples <strong>and</strong> then apply an appropriate two-dimensi<strong>on</strong>al filter. <str<strong>on</strong>g>Final</str<strong>on</strong>g>ly,<br />
interpolati<strong>on</strong> is used to find the value for a specific x<br />
i<br />
, yi<br />
. In this approach the resoluti<strong>on</strong> of the grid<br />
should be much finer that the correlati<strong>on</strong> distance λ c .<br />
After having obtained ( x, y)<br />
? the actual large-scale parameters are obtained as<br />
( µ )<br />
( ) = − 1 0.5<br />
x , y g R ( 0) ?( x,<br />
y)<br />
s +<br />
, (6.6)<br />
0.5<br />
T 0.5<br />
5<br />
where R ( 0)<br />
is obtained from the eigendecompositi<strong>on</strong> R( 0) = EΛE<br />
as R ( 0) = EΛ<br />
0.<br />
required parameters are found in Sectri<strong>on</strong> 3.<br />
, <strong>and</strong> the<br />
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6.2 Interfaces<br />
This secti<strong>on</strong> describes example input <strong>and</strong> output interfaces of the WIM <strong>channel</strong> model functi<strong>on</strong> in Matlab<br />
format.<br />
6.2.1 Example input parameters<br />
There are four input arguments, all of which are MATLAB structs. The first three arguments are<br />
m<strong>and</strong>atory. The following tables describe the fields of the input structs.<br />
Table 6.2: General <strong>channel</strong> model parameters. Comm<strong>on</strong> for all <strong>link</strong>s.<br />
Parameter name Definiti<strong>on</strong> Default value Unit<br />
NumBsElements<br />
NumMsElements<br />
Scenario<br />
PropagC<strong>on</strong>diti<strong>on</strong><br />
SampleDensity<br />
NumTimeSamples<br />
UniformTimeSampling<br />
NumSubPathsPerPath<br />
FixedPdpUsed<br />
FixedAnglesUsed<br />
CenterFrequency<br />
The number of elements in the BS array. This<br />
parameter is ignored if antenna patterns are defined<br />
in the input struct ANTPAR. In this case the number<br />
of BS elements is extracted from the antenna<br />
definiti<strong>on</strong>.<br />
The number of elements in the MS array. This<br />
parameter is ignored if antenna patterns are defined<br />
in the input struct ANTPAR. In this case the number<br />
of BS elements is extracted from the antenna<br />
definiti<strong>on</strong>.<br />
Selected WIM <strong>channel</strong> scenario (‘A1’, ‘B1’, ‘C2’ or<br />
‘D1’)<br />
Line of sight c<strong>on</strong>diti<strong>on</strong> (‘LOS’, ’NLOS’). Select<br />
either line of sight or n<strong>on</strong> line of sight model.<br />
Time sampling interval of the <strong>channel</strong>. A value<br />
greater than <strong>on</strong>e should be selected if Doppler<br />
analysis is to be d<strong>on</strong>e.<br />
Number of <strong>channel</strong> samples (impulse resp<strong>on</strong>se<br />
matrices) to generate per <strong>link</strong>.<br />
If this parameter has value ‘yes’ time sampling<br />
interval of the <strong>channel</strong> for each user will be equal.<br />
Sampling interval will be calculated from the<br />
SampleDensity <strong>and</strong> the highest velocity found in the<br />
input parameter vector MsVelocity. If this<br />
parameter has value ’no’, then the time sampling<br />
interval for each <strong>link</strong> will be different, if MSs have<br />
different speeds (see userpar.MsVelocity). Setting<br />
this parameters ‘yes’ may be useful in some <strong>system</strong><strong>level</strong><br />
simulati<strong>on</strong>s where all simulated <strong>link</strong>s need to<br />
be sampled at equal time intervals, regardless of MS<br />
speeds.<br />
Number of rays per path. The <strong>on</strong>ly value supported<br />
in the WIM implementati<strong>on</strong> is 10 rays.<br />
Use tabulated delays instead of drawing r<strong>and</strong>om<br />
values for each drop yes/no. If FixedPdpUsed='yes',<br />
the delays <strong>and</strong> powers of paths are taken from a<br />
table.<br />
Use tabulated angles instead of drawing r<strong>and</strong>om<br />
values for each drop yes/no. If<br />
FixedAnglesUsed='yes', the AoD/AoAs are taken<br />
from a table. R<strong>and</strong>om pairing of AoDs <strong>and</strong> AoAs is<br />
not used.<br />
Carrier centre-frequency. Affects path loss <strong>and</strong> time<br />
sampling interval.<br />
2<br />
2<br />
‘A1’<br />
‘NLOS’ -<br />
2<br />
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-<br />
samples/half<br />
wavelength<br />
100 -<br />
‘no’ -<br />
10 -<br />
‘no’ -<br />
‘no’ -<br />
DelaySamplingInterval Delay sampling interval (delay resoluti<strong>on</strong>). 10e-8 sec<br />
PathLossModelUsed<br />
Path-loss included in the <strong>channel</strong> matrices yes/no (if<br />
‘no’, PL is calculated <strong>and</strong> returned in the sec<strong>on</strong>d<br />
output argument, but not multiplied with the<br />
<strong>channel</strong> matrices)<br />
2E9<br />
Hz<br />
‘no’ -<br />
ShadowingModelUsed Shadow fading included in the <strong>channel</strong> matrices ‘no’ -
WINNER D5.4 v. 1.4<br />
PathLossModel<br />
AnsiC_core<br />
LookUpTable<br />
R<strong>and</strong>omSeed<br />
yes/no (if ‘no’ shadow fading is still computed <strong>and</strong><br />
returned in the sec<strong>on</strong>d output argument, but not<br />
multiplied with the <strong>channel</strong> matrices). Note that if<br />
both path loss <strong>and</strong> shadowing are switched off the<br />
average power of the <strong>channel</strong> matrix elements will<br />
be <strong>on</strong>e (with azimuthally uniform unit gain<br />
antennas).<br />
The name of the path-loss functi<strong>on</strong>. Functi<strong>on</strong> ‘pathloss’<br />
implements the default WIM path-loss model.<br />
If the default is used, centre-frequency is taken from<br />
the parameter CenterFrequency. One can define<br />
his/her own path-loss functi<strong>on</strong>. For syntax, see<br />
PATHLOSS.<br />
Use optimized computati<strong>on</strong> yes/no. With ‘yes’<br />
faster C-functi<strong>on</strong> is used instead of m-functi<strong>on</strong>.<br />
Note the C-functi<strong>on</strong> SCM_MEX_CORE.C must be<br />
compiled before usage. For more informati<strong>on</strong>, see<br />
SCM_MEX_CORE.M.<br />
If optimized computati<strong>on</strong> is used, complex<br />
exp<strong>on</strong>ents can be either taken from a look-up table<br />
to speed up computati<strong>on</strong> or calculated explicitly.<br />
This parameter defines the table size, if 0 table is<br />
not used, if –1 default table size 2 14 =16384 is used.<br />
The size of the table must be a power-of-two. If<br />
AnsiC_core = ‘no’ this parameter is ignored.<br />
R<strong>and</strong>om seed for fully repeatable <strong>channel</strong><br />
generati<strong>on</strong> (if empty, seed is generated r<strong>and</strong>omly).<br />
Note that even if R<strong>and</strong>omSeed is the fixed, two<br />
<strong>channel</strong> realizati<strong>on</strong>s may still not be the same<br />
between different MATLAB versi<strong>on</strong>s.<br />
‘path-loss’ -<br />
‘no’ -<br />
0 integer<br />
integer<br />
Table 6.3: Link-dependent parameters. All the parameters are vectors of length K, where K is the<br />
number of <strong>link</strong>s. The values are r<strong>and</strong>omly generated; they are not based <strong>on</strong> any specific network<br />
geometry or user behaviour model.<br />
Parameter name<br />
6.2.1.1 Definiti<strong>on</strong><br />
Default value<br />
MsBsDistance Distance between BS <strong>and</strong> MS 1965*RAND(1,K) + 35 m<br />
ThetaBs θ BS (see Figure 6.2) 360* RAND(1,K) deg<br />
ThetaMs θ MS (see Figure 6.2) 360* RAND(1,K) deg<br />
MsVelocity MS velocity 10 m/s<br />
MsDirecti<strong>on</strong> θ v (see Figure 6.2) 360* RAND(1,K) deg<br />
BsNumber<br />
StreetWidth<br />
Dist2<br />
MsNumber is a positive integer defining the index<br />
number of MS for each <strong>link</strong>. This parameter is used in<br />
generati<strong>on</strong> of inter-site correlated shadow fading<br />
values; shadow fading is correlated for <strong>link</strong>s between a<br />
single MS <strong>and</strong> multiple BSs. There is no correlati<strong>on</strong> in<br />
shadow fading between different MSs. Examples: The<br />
default value is the case where all <strong>link</strong>s in a call to the<br />
SCM functi<strong>on</strong> corresp<strong>on</strong>d to different MSs. Setting<br />
MsNumber=<strong>on</strong>es(1,K) corresp<strong>on</strong>ds to the case where<br />
the <strong>link</strong>s from a single MS to K different BSs are<br />
simulated.<br />
Street width is utilized <strong>on</strong>ly with path-loss model in<br />
[D5.3, sec 2.3.1.13.2]<br />
This is utilized <strong>on</strong>ly with path-loss model in [D5.3, sec<br />
2.3.1.13.2]. Parameter is defined in Figure 2-37 in<br />
[D5.3] <strong>and</strong> generated r<strong>and</strong>omly if empty.<br />
Unit<br />
[1:K] -<br />
20 m<br />
[empty matrix] 1xK<br />
m<br />
Table 6.4: Antenna parameters. The following parameters characterize the antennas. Currently<br />
<strong>on</strong>ly linear uniform arrays with dual-polarized elements are supported. The antenna patterns do<br />
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not have to be identical. The complex field pattern values for the r<strong>and</strong>omly generated AoDs <strong>and</strong><br />
AoAs are interpolated.<br />
Parameter name Definiti<strong>on</strong> Default value Unit<br />
BS antenna field pattern values in a 4D array. The<br />
dimensi<strong>on</strong>s are [ELNUM POL EL AZ] =<br />
SIZE(BsGainPattern), where<br />
BsGainPattern<br />
ELNUM is the number of physical antenna elements in<br />
the array. The elements may be dual-polarized.<br />
POL – polarizati<strong>on</strong>. The first dimensi<strong>on</strong> is vertical<br />
polarizati<strong>on</strong>, the sec<strong>on</strong>d is horiz<strong>on</strong>tal. If the polarizati<strong>on</strong><br />
opti<strong>on</strong> is not used, vertical polarizati<strong>on</strong> is assumed (if both<br />
are given).<br />
EL – elevati<strong>on</strong>. This value is ignored. Only the first<br />
element of this dimensi<strong>on</strong> is used.<br />
AZ – complex-valued field pattern in the azimuth<br />
dimensi<strong>on</strong> given at azimuth angles defined in<br />
BsGainAnglesAz.<br />
1<br />
BsGainAnglesAz<br />
BsGainAnglesEl<br />
BsElementPositi<strong>on</strong><br />
If NUMEL(BsGainPattern)=1, all elements are assumed to<br />
have uniform gain defined by the value of BsGainPattern<br />
over the full azimuth angle, <strong>and</strong> the number of BS antenna<br />
elements is defined by NumBsElements. This speeds up<br />
computati<strong>on</strong> since field pattern interpolati<strong>on</strong> is not<br />
required.<br />
Vector c<strong>on</strong>taining the azimuth angles for the BS antenna<br />
field pattern values. These values are assumed to be the<br />
same for both polarizati<strong>on</strong>s. This value is given in degrees<br />
over the range (-180,180) degrees. If<br />
NUMEL(BsGainPattern)=1, this variable is ignored.<br />
Vector of elevati<strong>on</strong> angles for definiti<strong>on</strong> of BS antenna<br />
gain values. This parameter is for future needs <strong>on</strong>ly; its<br />
value is ignored in this implementati<strong>on</strong> (WIM does not<br />
support elevati<strong>on</strong>).<br />
Element spacing for BS linear array in wavelengths. This<br />
parameter can be either scalar or vector. If scalar, uniform<br />
spacing is applied. If vector, values give distances between<br />
adjacent elements.<br />
MS antenna field pattern values in a 4D array. The<br />
dimensi<strong>on</strong>s are [ELNUM POL EL AZ] =<br />
SIZE(MsGainPattern), where<br />
linspace(-<br />
180,180,90)<br />
deg<br />
- -<br />
0.5 wavelength<br />
MsGainPattern<br />
ELNUM – the number of physical antenna elements in the<br />
array. The elements may be dual-polarized.<br />
POL – polarizati<strong>on</strong>. The first dimensi<strong>on</strong> is vertical<br />
polarizati<strong>on</strong>, the sec<strong>on</strong>d is horiz<strong>on</strong>tal. If the polarizati<strong>on</strong><br />
opti<strong>on</strong> is not used, vertical polarizati<strong>on</strong> is assumed (if both<br />
are given).<br />
EL – elevati<strong>on</strong>. This value is ignored. Only the first<br />
element of this dimensi<strong>on</strong> is used.<br />
AZ – complex-valued field pattern in the azimuth<br />
dimensi<strong>on</strong> given at azimuth angles defined in<br />
MsGainAnglesAz.<br />
1 complex<br />
If NUMEL(MsGainPattern)=1, all elements are assumed<br />
to have uniform gain defined by the value of<br />
MsGainPattern over the full azimuth angle, <strong>and</strong> the<br />
number of MS antenna elements is defined by<br />
wimpar.NumMsElements. This speeds up computati<strong>on</strong><br />
since field pattern interpolati<strong>on</strong> is not needed.<br />
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MsGainAnglesAz<br />
MsGainAnglesEl<br />
MsElementPositi<strong>on</strong><br />
InterpFuncti<strong>on</strong><br />
InterpMethod<br />
Vector c<strong>on</strong>taining the azimuth angles for the MS antenna<br />
field pattern values. These values are assumed to be the<br />
same for both polarizati<strong>on</strong>s. This value is given in degrees<br />
over the range (-180,180) degrees. If<br />
NUMEL(BsGainPattern)=1, this variable is ignored.<br />
Vector of elevati<strong>on</strong> angles for definiti<strong>on</strong> of MS antenna<br />
gain values. This parameter is for future needs <strong>on</strong>ly; its<br />
value is ignored in this implementati<strong>on</strong> (WIM does not<br />
support elevati<strong>on</strong>).<br />
Element spacing for MS linear array in wavelengths. This<br />
parameter can be either scalar or vector. If scalar, uniform<br />
spacing is applied. If vector, values give distances between<br />
adjacent elements.<br />
The name of the interpolating functi<strong>on</strong>. One can replace<br />
this with his own functi<strong>on</strong>. For syntax, see interp_gain.m,<br />
which is the default functi<strong>on</strong>. For faster computati<strong>on</strong>, see<br />
interp_gain_c.m<br />
The interpolati<strong>on</strong> method used by the interpolating<br />
functi<strong>on</strong>. Available methods depend <strong>on</strong> the functi<strong>on</strong>. The<br />
default functi<strong>on</strong> is based <strong>on</strong> MATLAB’s interp1.m<br />
functi<strong>on</strong> <strong>and</strong> supports e.g. ‘linear’ <strong>and</strong> ‘cubic’ (default)<br />
methods. Note that some methods, such as ‘linear’, cannot<br />
extrapolate values falling outside the field pattern<br />
definiti<strong>on</strong>.<br />
linspace(-<br />
180,180,90)<br />
deg<br />
- -<br />
0.5 wavelength<br />
‘interp_gain’ -<br />
‘cubic’ -<br />
Parameter matrices BsGainPattern <strong>and</strong> MsGainPattern 2nd dimensi<strong>on</strong> is either 1 or 2. If polarizati<strong>on</strong><br />
opti<strong>on</strong> is in use, the field pattern values have to be given for vertical <strong>and</strong> horiz<strong>on</strong>tal polarizati<strong>on</strong>s (in this<br />
order). If polarizati<strong>on</strong> is not used <strong>on</strong>ly the first dimensi<strong>on</strong>, i.e. vertical, is used, if both are given.<br />
Note that the mean power of narrowb<strong>and</strong> <strong>channel</strong> matrix elements (i.e. summed over delay domain)<br />
depends <strong>on</strong> the antenna gains. The default antenna has unit gain for both polarizati<strong>on</strong>s. Hence, the mean<br />
narrowb<strong>and</strong> <strong>channel</strong> coefficient power is two for ‘polarized’ opti<strong>on</strong>, <strong>and</strong> <strong>on</strong>e for all other opti<strong>on</strong>s.<br />
The fourth input argument, is opti<strong>on</strong>al. It can be used to specify the initial AoDs, AoAs, cisoid phases,<br />
path losses <strong>and</strong> shadowing values when WIM is called recursively, or for testing purposes. If this<br />
argument is given, the r<strong>and</strong>om parameter generati<strong>on</strong> as defined in WIM is not needed. Only the antenna<br />
gain values will be interpolated for the supplied AoAs <strong>and</strong> AoDs.<br />
The fields of the MATLAB struct are given in the following table. Notati<strong>on</strong>: K denotes the number of<br />
<strong>link</strong>s, N denotes the number of paths, M denotes the number of subpaths within a path.<br />
Table 6.5: Initial values, fourth opti<strong>on</strong>al input argument.<br />
Parameter name Definiti<strong>on</strong> Unit<br />
InitDelays A K x N matrix of path delays. Sec<br />
InitSubPathPowers A K x N x M array of powers of the subpaths. -<br />
InitAods A K x N x M array Degrees<br />
InitAoas A K x N x M array Degrees<br />
InitSubPathPhases<br />
A complex-valued K x N x M array. When polarizati<strong>on</strong> opti<strong>on</strong><br />
is used, this is a K x P x N x M array, where P=4. In this case<br />
the sec<strong>on</strong>d dimensi<strong>on</strong> includes the phases for [VV VH HV<br />
HH] polarized comp<strong>on</strong>ents.<br />
degrees<br />
InitPathLosses A K x 1 vector Decibel<br />
InitShadowLosses A K x 1 vector Decibel<br />
6.2.2 Example output parameters<br />
There are three output arguments: CHAN, DELAYS, FULLOUTPUT. The last two are opti<strong>on</strong>al output<br />
parameters. Notati<strong>on</strong>: K denotes the number of <strong>link</strong>s, N is the number of paths, T the number of time<br />
samples, U the number of receiver elements, <strong>and</strong> S denotes the number of transmitter elements.<br />
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Table 6.6: The three output arguments.<br />
Parameter name Definiti<strong>on</strong> Unit<br />
CHAN<br />
DELAYS<br />
FULLOUTPUT<br />
delays<br />
A 5D-array with dimensi<strong>on</strong>s U x S x N x T x K<br />
A K x N vector of path delay values. Note that delays<br />
are, for compatibility with the INITVALUES, also<br />
included in FULLOUTPUT.<br />
A MATLAB struct with the following elements:<br />
A K x N matrix of path delays. This is identical to the<br />
sec<strong>on</strong>d output argument.<br />
subPathPowers A K x N x M array of subpath powers. -<br />
Aods A K x N x M array of subpath angles of departure degrees<br />
Aoas A K x N x M array of subpath angles of arrival degrees<br />
subpath_phases<br />
A complex-valued K x N x M array giving the final<br />
phases of all subpaths. When polarizati<strong>on</strong> opti<strong>on</strong> is<br />
used, a K x P x N x M array, where P=4. In this case<br />
the sec<strong>on</strong>d dimensi<strong>on</strong> includes the phases for [VV VH<br />
HV HH] polarized comp<strong>on</strong>ents.<br />
sec<br />
sec<br />
degrees<br />
Path_losses A K x 1 vector linear scale<br />
shadow_fading A K x 1 vector linear scale<br />
Delta_t<br />
Xpr<br />
A K x 1 vector defining time sampling interval for all<br />
<strong>link</strong>s.<br />
A K x 2 x N array of cross-polarizati<strong>on</strong> coupling<br />
power ratios. The sec<strong>on</strong>d dimensi<strong>on</strong> is the [V-to-H H-<br />
to-V] coupling ratios.<br />
sec<br />
linear scale<br />
6.3 Guidelines <strong>and</strong> examples <strong>on</strong> performing <strong>system</strong>-<strong>level</strong> simulati<strong>on</strong>s<br />
Chapter 6.1 has provided an overview <strong>on</strong> the c<strong>on</strong>cept of our implementati<strong>on</strong>. In the following, we want to<br />
show how this generic interface can be used to simulate some special types of <strong>system</strong>-<strong>level</strong> situati<strong>on</strong>s.<br />
This can serve as a quick guide <strong>on</strong> how to implement these special cases <strong>and</strong> as proof of the versatility of<br />
our implementati<strong>on</strong>.<br />
6.3.1 H<strong>and</strong>over<br />
A h<strong>and</strong>over situati<strong>on</strong> is characterized by a MS moving from the coverage are of <strong>on</strong>e BS to the coverage<br />
area of another BS. Figure 6.3 illustrates this setup.<br />
Figure 6.3: H<strong>and</strong>over scenario.<br />
There are two base-stati<strong>on</strong>s or cells denoted c1 <strong>and</strong> c2, <strong>and</strong> <strong>on</strong>e mobile stati<strong>on</strong>. Note that WIM is a quasistati<strong>on</strong>ary<br />
<strong>channel</strong> model; it does not provide the means to generate smooth evoluti<strong>on</strong> of <strong>channel</strong>s for a<br />
l<strong>on</strong>g, c<strong>on</strong>tinuous period. What we generate instead is the <strong>channel</strong>s for a sequence of short, separated<br />
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periods. Path-loss will be determined according to the geometry, large-scale parameters correlate<br />
properly, but first-order bulk parameters change abruptly from a segment to segment. Thus, while there is<br />
<strong>on</strong>ly <strong>on</strong>e mobile stati<strong>on</strong> in the scenario, each locati<strong>on</strong> of the mobile <strong>on</strong> its path is assigned a unique label<br />
ms1 to msM. This is equivalent to a scenario with multiple mobile stati<strong>on</strong>s at different positi<strong>on</strong>s ms1 to<br />
msM. The resulting procedure is as follows.<br />
1. Set base stati<strong>on</strong> c1 <strong>and</strong> c2 locati<strong>on</strong>s <strong>and</strong> array orientati<strong>on</strong>s according to geometry.<br />
2. Set MS locati<strong>on</strong>s ms1 to msM <strong>and</strong> array orientati<strong>on</strong>s al<strong>on</strong>g the route. Choose the distance<br />
between adjacent locati<strong>on</strong>s according to desired accuracy.<br />
3. Set all the entries of the pairing matrix to 1.<br />
4. Generate all the radio <strong>link</strong>s at <strong>on</strong>ce obtain correct correlati<strong>on</strong> properties. It is possible to generate<br />
more <strong>channel</strong> realizati<strong>on</strong>s, i.e. time samples, for each <strong>channel</strong> segment afterwards. This can be<br />
d<strong>on</strong>e by applying the initial values of small scale parameters in the Table 6.5.<br />
5. Simulate <strong>channel</strong> segments c<strong>on</strong>secutively to emulate moti<strong>on</strong> al<strong>on</strong>g the route.<br />
6.3.2 Interference<br />
Interference situati<strong>on</strong>s are quite similar to h<strong>and</strong>over situati<strong>on</strong>s, except that in this case the sec<strong>on</strong>d BS<br />
transmits a n<strong>on</strong>-desired signal which creates interference. That doesn’t change the parameters of the<br />
<strong>channel</strong>. What changes, however, is the degree of realism that is needed for the interference <strong>channel</strong>. This<br />
has been discussed in Chapter 4.1.3.<br />
6.3.3 Multi-cell <strong>and</strong> multi-user<br />
The h<strong>and</strong>over <strong>and</strong> interference situati<strong>on</strong>s from the previous secti<strong>on</strong>s were an example of single-user<br />
multi-cell setups. Other cases of such a setup are for example found in the c<strong>on</strong>text of multi-BS protocols,<br />
where a MS receives data from multiple BS simultaneously.<br />
The extensi<strong>on</strong> to multiple users (<strong>and</strong> <strong>on</strong>e or more base stati<strong>on</strong>s) is straightforward. Because locati<strong>on</strong> <strong>and</strong><br />
mobile stati<strong>on</strong> index are treated equivalently, it follows that all locati<strong>on</strong>s of all mobiles have to be<br />
defined. C<strong>on</strong>sider the drive-by situati<strong>on</strong> in Figure 6.4.<br />
Figure 6.4: Drive-by scenario (with multiple mobile stati<strong>on</strong>s).<br />
Here, M locati<strong>on</strong>s of mobile stati<strong>on</strong> 1, <strong>and</strong> N locati<strong>on</strong>s of mobile stati<strong>on</strong> 2 are defined yielding a total of<br />
M+N points or labels. The resulting procedure is as follows.<br />
1. Set BS c1 <strong>and</strong> c2 locati<strong>on</strong>s <strong>and</strong> array orientati<strong>on</strong>s according to layout.<br />
2. Set MS locati<strong>on</strong>s ms11 to ms2N <strong>and</strong> array orientati<strong>on</strong>s according to layout.<br />
3. Set the <strong>link</strong>s to be modelled to 1 in the pairing matrix.<br />
4. Generate all the radio <strong>link</strong>s at <strong>on</strong>ce obtain correct correlati<strong>on</strong> properties. It is possible to generate<br />
more <strong>channel</strong> realizati<strong>on</strong>s, i.e. time samples, for each <strong>channel</strong> segment afterwards. This can be<br />
d<strong>on</strong>e by applying the initial values of small scale parameters in the Table 6.5.<br />
5. Simulate <strong>channel</strong> segments in parallel or c<strong>on</strong>secutively according to the desired moti<strong>on</strong> of the<br />
mobiles.<br />
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6.3.4 Multihop <strong>and</strong> relaying<br />
Typically, the <strong>link</strong>s between the MS <strong>and</strong> the <strong>link</strong>s between the BS are not of interest. Cellular <strong>system</strong>s are<br />
traditi<strong>on</strong>ally centric networks where all traffic goes through <strong>on</strong>e or more BS. The BS themselves again<br />
<strong>on</strong>ly talk to a BS hub <strong>and</strong> not between them.<br />
Multihop <strong>and</strong> relaying networks break with this limitati<strong>on</strong>. In multihop networks, the data can take a route<br />
over <strong>on</strong>e or more successive MS. Relaying networks, <strong>on</strong> the other h<strong>and</strong>, employ another <strong>level</strong> of network<br />
stati<strong>on</strong>s, the relays, which depending <strong>on</strong> the specific network, might offer more or less functi<strong>on</strong>ality to<br />
distribute traffic intelligently.<br />
<<br />
06<br />
%6<br />
%6<br />
06<br />
06<br />
%6<br />
%6<br />
06<br />
;<br />
Figure 6.5: Multihop <strong>and</strong> relaying scenarios.<br />
In the example figure above the signal from MS1 to BS3 is transmitted via MS3 <strong>and</strong> BS2 act as a repeater<br />
for BS1. These scenarios can be generated by introducing a BS-MS pair into positi<strong>on</strong> of a single BS<br />
serving as a relay or into positi<strong>on</strong> of a single MS serving as a multihop repeater. In these cases <strong>on</strong>e can<br />
apply path-loss <strong>models</strong> of feeder scenarios described in secti<strong>on</strong> 3.2.4. The resulting procedure is as<br />
follows.<br />
1. Set base stati<strong>on</strong> BS1 to BS3 locati<strong>on</strong>s <strong>and</strong> array orientati<strong>on</strong>s according to layout.<br />
2. Set mobile locati<strong>on</strong>s MS1 to MS3 <strong>and</strong> array orientati<strong>on</strong>s according to layout.<br />
3. Add extra base stati<strong>on</strong> BS4 to positi<strong>on</strong> of MS3 <strong>and</strong> extra mobile MS4 to positi<strong>on</strong> of BS2 with<br />
same array orientati<strong>on</strong>s <strong>and</strong> array characteristics as MS3 <strong>and</strong> BS2 respectively.<br />
4. Set the pairing matrix to<br />
⎡0<br />
1 0 0⎤<br />
⎢ ⎥<br />
⎢<br />
0 0 0 1<br />
A =<br />
⎥<br />
⎢0<br />
0 1 0⎥<br />
⎢ ⎥<br />
⎣1<br />
0 0 0⎦<br />
5. Generate all the radio <strong>link</strong>s at <strong>on</strong>ce.<br />
6. Simulate the <strong>channel</strong> segments in parallel.<br />
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7. Test <strong>and</strong> Verificati<strong>on</strong> of the Channel Model <strong>and</strong> Its Implementati<strong>on</strong><br />
The goal of <strong>channel</strong> modelling is to imitate real radio <strong>channel</strong> with high accuracy but with low<br />
complexity. WINNER <strong>channel</strong> model parameters are based <strong>on</strong> measurements c<strong>on</strong>ducted during the<br />
project <strong>and</strong> prior measurement results available <strong>on</strong> public literature. The ultimate verificati<strong>on</strong> of the<br />
model would be to compare the <strong>channel</strong> model output parameters to measurement results. Practical<br />
comparis<strong>on</strong> should be d<strong>on</strong>e between the statistical distributi<strong>on</strong>s of the <strong>channel</strong> parameters. To perform<br />
any verificati<strong>on</strong>, we need implementati<strong>on</strong> of the model.<br />
Verificati<strong>on</strong> of the WINNER <strong>channel</strong> model is a twofold task. The first task is to test the implementati<strong>on</strong>,<br />
i.e. to verify that implementati<strong>on</strong> is in line with the model descripti<strong>on</strong>. WINNER <strong>channel</strong> model has three<br />
<strong>level</strong>s: generati<strong>on</strong> of large-scale parameters like sec<strong>on</strong>d moments of delay <strong>and</strong> directi<strong>on</strong> distributi<strong>on</strong>s,<br />
generati<strong>on</strong> of small scale parameters like delays <strong>and</strong> mean powers, <strong>and</strong> generati<strong>on</strong> of matrix coefficient<br />
<strong>channel</strong> impulse resp<strong>on</strong>ses for the radio <strong>link</strong>s. Testing of implementati<strong>on</strong> is mostly verifying generati<strong>on</strong> of<br />
large-scale parameters. This work is <str<strong>on</strong>g>report</str<strong>on</strong>g>ed in [WP5TS].<br />
The sec<strong>on</strong>d task is to compare implementati<strong>on</strong> output to measurement results. Statistical distributi<strong>on</strong>s of<br />
<strong>channel</strong> parameters can be extracted from the output of the implementati<strong>on</strong>. Extracti<strong>on</strong> can be d<strong>on</strong>e<br />
applying the same methods as with the measured radio <strong>channel</strong>. However comparis<strong>on</strong> of output statistics<br />
to measurement results is not feasible because of reduced complexity of the model. The number of<br />
<strong>channel</strong> multipath comp<strong>on</strong>ents is limited in the model compared to a number that <strong>on</strong>e can observe in the<br />
reality. Even though parameters of the multipath comp<strong>on</strong>ents are drawn from the specific distributi<strong>on</strong>, it<br />
is not possible to compute back the same distributi<strong>on</strong> from a very limited number of samples. Thus<br />
verificati<strong>on</strong> of the model against the measurements c<strong>on</strong>ducted in real envir<strong>on</strong>ments is not viable due to<br />
limited number of samples (multipath comp<strong>on</strong>ents per <strong>channel</strong> segment).<br />
7.1 Test cases<br />
7.1.1 General test cases<br />
All test cases assume reference MIMO antenna c<strong>on</strong>figurati<strong>on</strong> B <strong>and</strong> reference antenna field pattern I,<br />
unless otherwise menti<strong>on</strong>ed.<br />
7.1.1.1 General features<br />
Test id Test descripti<strong>on</strong> Expected outcome Notes<br />
1.1.A<br />
1.1.B<br />
1.1.C<br />
See that the installati<strong>on</strong> package (zip file)<br />
of the distributed versi<strong>on</strong> includes all the<br />
files <strong>and</strong> that the installati<strong>on</strong> instructi<strong>on</strong>s<br />
are intelligible <strong>and</strong> informative. No vital<br />
or important informati<strong>on</strong> is missing.<br />
Give suggesti<strong>on</strong>s for improvement.<br />
Installati<strong>on</strong> <strong>and</strong> compilati<strong>on</strong> of the<br />
optimized ANSI C core functi<strong>on</strong><br />
(scm_core.c) succeeds <strong>and</strong> is adequately<br />
documented.<br />
Check that MATLAB help texts of all<br />
functi<strong>on</strong>s are intelligible <strong>and</strong> informative.<br />
Functi<strong>on</strong>s are wim.m,<br />
generate_bulk_par.m, path-loss.m,<br />
wimparset.m, <strong>link</strong>parset.m, antparset.m,<br />
scenpartables.m<br />
All files included,<br />
informative<br />
installati<strong>on</strong><br />
instructi<strong>on</strong>s within the<br />
distributi<strong>on</strong> zip file.<br />
Nothing vital missing.<br />
Installati<strong>on</strong> <strong>and</strong>/or<br />
compilati<strong>on</strong> is<br />
successful <strong>on</strong> various<br />
platfoRMS: Linux,<br />
Unix, Windows.<br />
Documentati<strong>on</strong> is<br />
sufficient.<br />
No mistakes or<br />
ambiguities in help<br />
texts.<br />
7.1.2 Input/output parameters<br />
7.1.2.1 Validity <strong>and</strong> range of basic input <strong>and</strong> output arguments<br />
Test id Test descripti<strong>on</strong> Expected outcome Notes<br />
2.1.A<br />
Field names of all input <strong>and</strong> output<br />
parameters corresp<strong>on</strong>d to those in the<br />
Full match between<br />
documentati<strong>on</strong><br />
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2.1.B<br />
2.1.C<br />
implementati<strong>on</strong> specificati<strong>on</strong>.<br />
Ranges, units <strong>and</strong> sizes of all input <strong>and</strong><br />
output parameters corresp<strong>on</strong>d to those in<br />
the implementati<strong>on</strong> specificati<strong>on</strong>.<br />
Angles units in the formulas <strong>and</strong> in the<br />
variables as input/ouput.<br />
functi<strong>on</strong> input <strong>and</strong><br />
output<br />
Full match between<br />
documentati<strong>on</strong><br />
functi<strong>on</strong> input <strong>and</strong><br />
output<br />
Angles in the formula<br />
should be in radians;<br />
angles in input/output<br />
should be in degrees;<br />
7.1.3 Validati<strong>on</strong> of computati<strong>on</strong><br />
7.1.3.1 Deterministic behaviour of MIMO <strong>channel</strong> matrices<br />
Test id Test descripti<strong>on</strong> Expected outcome Notes<br />
3.1.A<br />
3.1.B<br />
Test a SISO <strong>system</strong> with NumPaths=1<br />
<strong>and</strong> <strong>on</strong>ly <strong>on</strong>e subpath<br />
(NumSubPathsPerPath=1). These must<br />
be fed as initial values using the fourth<br />
input argument. Check that the amplitude<br />
of the <strong>channel</strong> coefficient over time is<br />
c<strong>on</strong>stant. Check that the phase of the<br />
<strong>channel</strong> coefficient changes as expected<br />
based <strong>on</strong> e.g. MSVelocity, array<br />
orientati<strong>on</strong> <strong>and</strong> AoA. Compute Doppler<br />
shift using FFT. Check that the shift is at<br />
the correct sideb<strong>and</strong> of the centrefrequency<br />
<strong>and</strong> of correct magnitude.<br />
Repeat 3.1.A for two subpaths coming<br />
from different AoAs.<br />
Amplitude is c<strong>on</strong>stant.<br />
Phase is changing<br />
accordingly. Doppler<br />
shift is correct.<br />
Amplitude is fading<br />
accordingly. Phase is<br />
changing accordingly.<br />
Doppler shifts of the<br />
subpaths are correct.<br />
In the test result<br />
descripti<strong>on</strong> of values<br />
used during testing <strong>and</strong><br />
expected output. For<br />
special settings, new<br />
scenario ‘Test’ must<br />
be added to the<br />
ScenParTables.m <strong>and</strong><br />
to the other functi<strong>on</strong>s.<br />
For special settings,<br />
new scenario ‘Test’<br />
must be added to the<br />
ScenParTables.m <strong>and</strong><br />
to the other functi<strong>on</strong>s.<br />
7.1.3.2 Stochastic behaviour of output MIMO <strong>channel</strong> matrices<br />
Test id Test descripti<strong>on</strong> Expected outcome Notes<br />
3.2.A<br />
3.2.A/L<br />
3.2.B<br />
3.2.B/L<br />
Mean power of each matrix element,<br />
summed over delay domain, should be<br />
<strong>on</strong>e.<br />
Narrowb<strong>and</strong> mean power for the LOS<br />
opti<strong>on</strong> should be <strong>on</strong>e for all matrix<br />
elements.<br />
Narrowb<strong>and</strong> amplitude distributi<strong>on</strong> of<br />
<strong>channel</strong> coefficients. Sum the <strong>channel</strong><br />
taps over delay domain for each time<br />
instant <strong>and</strong> each element of the MIMO<br />
matrix. The amplitude distributi<strong>on</strong> of<br />
each MIMO matrix element should be<br />
approximately Rayleigh.<br />
Narrowb<strong>and</strong> amplitude distributi<strong>on</strong> of<br />
<strong>channel</strong> coefficients for the LOS<br />
c<strong>on</strong>diti<strong>on</strong>. Sum the <strong>channel</strong> taps over<br />
delay domain for each time instant <strong>and</strong><br />
each element of the MIMO matrix. The<br />
amplitude distributi<strong>on</strong> of each MIMO<br />
matrix element should be approximately<br />
All matrix elements<br />
have unit narrow-b<strong>and</strong><br />
power.<br />
3.2.A<br />
Both cdf <strong>and</strong> pdf of<br />
narrowb<strong>and</strong> matrix<br />
elements are Rayleigh<br />
Both cdf <strong>and</strong> pdf of<br />
narrowb<strong>and</strong> matrix<br />
elements are Ricean<br />
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3.2.C<br />
Ricean with the corresp<strong>on</strong>ding K factor.<br />
Narrowb<strong>and</strong> phase angle distributi<strong>on</strong> of<br />
<strong>channel</strong> coefficients. Sum the <strong>channel</strong><br />
taps over delay domain for each time<br />
instant <strong>and</strong> each element of the MIMO<br />
matrix. The distributi<strong>on</strong> of the phase of<br />
each MIMO matrix element should be<br />
approximately uniform over (0,2*pi].<br />
Uniform pdf over<br />
(0,2*pi]<br />
7.1.3.3 Stochastic behaviour of the large-scale parameters<br />
Test id Test descripti<strong>on</strong> Expected outcome Notes<br />
3.3.A<br />
3.3.B<br />
3.3.C<br />
Bulk parameter statistics. Repeat all<br />
the results in Appendix 4.<br />
Path-loss <strong>models</strong> for all the scenarios<br />
except B1 NLOS. Repeat results in<br />
[D5.3, Sec. 2.3.1.13]. Calculate pathloss<br />
exp<strong>on</strong>ent <strong>and</strong> intercept <strong>and</strong><br />
compare to given values.<br />
Path-loss model for B1 NLOS. Fit a<br />
plane to resulting<br />
triplets.<br />
( log10<br />
d<br />
1,log10<br />
d<br />
2,<br />
PL)<br />
Compare the coefficients of plane<br />
equati<strong>on</strong> to values based <strong>on</strong> [D5.3, eq.<br />
2.6].<br />
The obtained results<br />
are very close to the<br />
‘input’ values.<br />
The obtained results<br />
are very close to<br />
[D5.3, sec. 2.3.1.13].<br />
Coefficients should<br />
be close to:<br />
a = 20.1<br />
b = 35.97<br />
c = 9.55<br />
Testing of mu, epsil<strong>on</strong>, <strong>and</strong> r<br />
values may be easier to do<br />
“within” the code (after step<br />
3) than from the output<br />
AoDs/AoAs. Note that, for<br />
example,<br />
E[log10(sigma_AS)]=mu_AS<br />
<strong>and</strong> STD(log10(sigma_AS))<br />
= epsil<strong>on</strong>_AS.<br />
The general equati<strong>on</strong> of<br />
plane:<br />
z = a*x + b*y + c<br />
7.1.3.4 Stochastic behaviour of CDL <strong>models</strong> output<br />
Test id Test descripti<strong>on</strong> Expected outcome Notes<br />
3.4.A<br />
3.4.B<br />
Estimate power delay profile from output<br />
<strong>channel</strong> matrices. Compare it to the <strong>on</strong>es<br />
given in [D5.3, Tables 4.7-16].<br />
Estimate amplitude probability density<br />
functi<strong>on</strong>s for <strong>models</strong>/clusters with LOS<br />
comp<strong>on</strong>ent. Compare distributi<strong>on</strong>s to<br />
Ricean distributi<strong>on</strong>s with desired K-<br />
factor.<br />
Resulting PDPs should<br />
match to <strong>on</strong>es given in<br />
[D5.3, Tables 4.7-16].<br />
Estimated PDFs<br />
should match<br />
theoretical <strong>on</strong>es.<br />
CDL <strong>models</strong> are<br />
selected by setting<br />
fixed PDP <strong>and</strong> Angles<br />
<strong>on</strong>.<br />
Theoretical PDFs must<br />
be generated by the<br />
test pers<strong>on</strong>.<br />
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9. Appendix<br />
9.1 Other scenarios<br />
9.1.1 Scenario definiti<strong>on</strong>s<br />
Here we present WP5 view to the envir<strong>on</strong>ments bey<strong>on</strong>d the five prioritized scenarios.<br />
9.1.1.1 Scenario “high mobility short range hot spot”<br />
This scenario is new <strong>and</strong> therefore not described in [D7.2]. We call it “High Mobility Short Range Hot<br />
Spot”. It represents an outdoor hot spot applicati<strong>on</strong> for short range distances, where the distance can be<br />
range from few meters to 250m. The Rx can be fixed e.g. <strong>on</strong> the side or even under a bridge. The Tx is<br />
mounted <strong>on</strong> the roof of a car, van or lorry. Within this scenario high mobility <strong>and</strong> traffic throughput with<br />
high density can be expected. Such scenarios can be found in rural, urban <strong>and</strong> suburban envir<strong>on</strong>ments,<br />
e.g. for traffic informati<strong>on</strong> or other user applicati<strong>on</strong>s. Depending <strong>on</strong> the envir<strong>on</strong>ment different MIMO<br />
<strong>channel</strong> characteristics must be c<strong>on</strong>sidered.<br />
Technische Universität Ilmenau (TUI) measured this particular scenario “High Mobility Short Range Hot<br />
Spot”, where the measurement car (Tx) was driving <strong>on</strong> a highway <strong>and</strong> the Rx was fixed at a bridge in a<br />
rural envir<strong>on</strong>ment. No buildings were in this area <strong>on</strong>ly high trees <strong>and</strong> shrubs surrounding the highway <strong>and</strong><br />
bridge. Both LOS <strong>and</strong> NLOS propagati<strong>on</strong> situati<strong>on</strong>s can be expected. Mostly LOS is obvious when the<br />
car is approaching the bridge, under <strong>and</strong> after the bridge NLOS is dominating.<br />
9.1.1.2 Scenario “outdoor to indoor”<br />
See [ZJ05].<br />
9.2 Measurement campaigns for other scenarios<br />
9.2.1 Scenario “high mobility short range hot spot”<br />
9.2.1.1 TUI campaign<br />
TUI outdoor high mobility short range hot spot measurement scenario c<strong>on</strong>sists of a bridge-to-car scenario<br />
in a highway, shown in Figure 9.1. These measurements were d<strong>on</strong>e with RUSK ATM MIMO sounder by<br />
Medav [Medav]. The Carrier frequency in the measurements was 5.2 GHz <strong>and</strong> b<strong>and</strong>width of 120 MHz.<br />
During the measurement campaign the Tx was driving <strong>on</strong> the right <strong>and</strong> left side lane of a two lane<br />
highway road (2 lanes per directi<strong>on</strong>). The Tx antenna was mounted <strong>on</strong> the roof of a car <strong>and</strong> the Rx as<br />
mounted at the bridge, whereby it was down tilt by 45 degrees. Pictures of high-resoluti<strong>on</strong> antennas used<br />
in TUI measurement campaign are shown in Figure 5.7. The maximum distance between Tx <strong>and</strong> Rx<br />
antenna positi<strong>on</strong> was found to be 250m, which defines the short range area for the hot spot applicati<strong>on</strong>.<br />
Figure 9.1: Bridge-to-car hot spot scenario.<br />
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9.2.2 Urban ad-hoc peer-to-peer<br />
9.2.2.1 NOK <strong>and</strong> HUT campaign<br />
In urban ad hoc peer-to-peer measurements both the receiver <strong>and</strong> transmitter ends of the radio <strong>link</strong> were<br />
equipped with spherical array antennas shown in Figure 5.8. Both Tx <strong>and</strong> Rx were installed to a trolley,<br />
<strong>and</strong> during the measurements the transmitter was kept fixed while the receiver unit was moving. The Tx<br />
<strong>and</strong> Rx antenna heights were ~1.5 m from the ground <strong>level</strong>. Measurements c<strong>on</strong>sist of 10 to 20 meter l<strong>on</strong>g<br />
routes in LOS <strong>and</strong> NLOS scenarios in urban outdoor <strong>and</strong> indoor envir<strong>on</strong>ments. Surrounding buildings<br />
were 4-6 floors high, <strong>and</strong> the Rx <strong>and</strong> Tx positi<strong>on</strong>s were located outdoors in open areas (market square),<br />
street cany<strong>on</strong>s <strong>and</strong> restaurant terrace. Outdoor-to-indoor peer-to-peer measurements were d<strong>on</strong>e in metro<br />
stati<strong>on</strong>, <strong>and</strong> indoor peer-to-peer cases were measured in metro stati<strong>on</strong> lobby <strong>and</strong> in a supermarket.<br />
9.3 Measurement results for other scenarios<br />
9.3.1 Scenario C2: typical urban macro-cell - KTH campaign<br />
These results are narrow b<strong>and</strong> measurements close to 2 GHz frequency range.<br />
9.3.1.1 Inter-sector <strong>and</strong> inter-site correlati<strong>on</strong>s<br />
The log-fading based <strong>on</strong> the four MS transmit antennas, as well as combing all MS transmit antennas<br />
together to form a basically omni-directi<strong>on</strong>al antenna, has been calculated. The correlati<strong>on</strong> of the logfading<br />
between sectors is shown in Table 9.1. The correlati<strong>on</strong> between Sector A <strong>and</strong> C has been excluded<br />
as it c<strong>on</strong>tains much less data than the correlati<strong>on</strong> between Sector A <strong>and</strong> B. The cross-site correlati<strong>on</strong> (A<br />
<strong>and</strong> B) is virtually very small while it is substantial for the cross-sector (B <strong>and</strong> C) measurement although<br />
not full.<br />
Table 9.1: Correlati<strong>on</strong> of log-normal fading.<br />
MS Antenna<br />
Sectors 1 2 3 4 All<br />
A & B 27% 7% 9% -9% 10%<br />
B & C 86% 77% 84% 77% 84%<br />
The correlati<strong>on</strong> of the angle-spread results is shown in Table 9.2. The correlati<strong>on</strong> of the angle-spread is<br />
virtually zero between the sites <strong>and</strong> very small between the sectors.<br />
Table 9.2: Correlati<strong>on</strong> of angle-spread.<br />
MS Antenna<br />
Sectors 1 2 3 4 All<br />
A & B 3% -2% -5% -19% -3%<br />
B & C 25% 17% 24% 26% 34%<br />
The reas<strong>on</strong> for the n<strong>on</strong>-full correlati<strong>on</strong> between the sectors of the same site (B & C) we believe is<br />
primarily due to the difference in base-stati<strong>on</strong> antenna pattern differences between the two sectors <strong>and</strong><br />
sec<strong>on</strong>dly because the two sectors are mounted 20-meters apart. Note that the sector cross correlati<strong>on</strong> is<br />
evaluated in an angle segment of width 20-degrees where the element patterns are oscillating. If the<br />
sectors had been more closely located <strong>and</strong> pointing in more similar directi<strong>on</strong>s we believe the correlati<strong>on</strong><br />
would be full. The correlati<strong>on</strong> between A <strong>and</strong> B sectors was evaluated mostly in the main beam of the two<br />
sectors.<br />
In [Maw92] the correlati<strong>on</strong> between sites of the log-normal fading is experimentally found to be given<br />
approximately 0.9-|θ |/200, where θ is the angle between the two base-stati<strong>on</strong>s as seen from the mobile<br />
stati<strong>on</strong>. For this measurement campaign this would corresp<strong>on</strong>d <strong>on</strong> average to a correlati<strong>on</strong> coefficient of<br />
40%. However, the correlati<strong>on</strong> herein is much lower.<br />
Even if the properties at the base-stati<strong>on</strong>s are different it could be theorized that the <strong>channel</strong>s at the<br />
mobile-stati<strong>on</strong>s were similar, for instance if the same scatterers are active in both c<strong>on</strong>necti<strong>on</strong>s. This<br />
property was investigated by indicating <strong>on</strong> map where the same mobile-stati<strong>on</strong> antenna is str<strong>on</strong>gest<br />
(summed over all transmit antennas) for the <strong>link</strong>s to sector A <strong>and</strong> B (i.e. different sites) in Figure 9.2. The<br />
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results show no indicati<strong>on</strong> of any correlati<strong>on</strong>. The corresp<strong>on</strong>ding plot for the cross-sector correlati<strong>on</strong> is<br />
shown in Figure 9.3. The cross-site results in Figure 9.2 show no correlati<strong>on</strong>. The same antenna was<br />
selected in <strong>on</strong>ly 29% of the cases. In fact, since not every antenna is selected with equal probability<br />
(probably due to unintended differences in tilt angle <strong>and</strong> reflecti<strong>on</strong>s from the car), the 29% is c<strong>on</strong>sistent<br />
with completely uncorrelated antenna selecti<strong>on</strong>. In Figure 9.3 the cross-sector results are shown. Here, the<br />
correlati<strong>on</strong> for distances larger than 300meters or so is almost full. The differences close to the basestati<strong>on</strong><br />
may be due to 20meter distance between the two sector antennas.<br />
Figure 9.2: Illustrati<strong>on</strong> of where the same MS<br />
antenna is the str<strong>on</strong>gest in sector A <strong>and</strong> B (which<br />
are <strong>on</strong> different sites).<br />
Figure 9.3: Illustrati<strong>on</strong> of where the same MS<br />
antenna is the str<strong>on</strong>gest in sector B <strong>and</strong> C<br />
(different sectors <strong>on</strong> same site).<br />
9.3.1.2 (Joint) DoA/DoD distributi<strong>on</strong>s<br />
The main DoA directi<strong>on</strong> is estimated for each local area by means of beamforming (the pointing directi<strong>on</strong><br />
in which the most energy is received). Since there are four mobile antennas four estimates are available<br />
from each of the mobile stati<strong>on</strong> transmitting antennas. Here we investigate the dependence between the<br />
pointing angle of the four mobile stati<strong>on</strong> antennas relative to the directi<strong>on</strong> of the base-stati<strong>on</strong>, a, <strong>and</strong> the<br />
DoA of the incoming signal ß, relative to the geographical angle, a, of the mobile, see Figure 9.4. The<br />
angle a is obtained by combining the estimated main DoA <strong>and</strong> the GPS informati<strong>on</strong>, while ß is obtained<br />
from the GPS informati<strong>on</strong> together with knowledge of the directi<strong>on</strong> of travel <strong>and</strong> the mounting of<br />
antennas <strong>on</strong> the vehicle.<br />
Figure 9.4: Illustrati<strong>on</strong> of the dependence between the pointing angle of the MS antenna (relative<br />
directi<strong>on</strong> of BS) <strong>and</strong> the main DoA at the base-stati<strong>on</strong>.<br />
If a <strong>on</strong>ce-bounce model is valid, as indicated in the figure, then a positive a should imply a negative ß. To<br />
investigate this c<strong>on</strong>jecture Figure 9.5 <strong>and</strong> Figure 9.6 were generated where the x-value of each 'x' marks<br />
the pointing directi<strong>on</strong> of an MS antenna, a, <strong>and</strong> the y-axis the main DoA offset ß estimated at the basestati<strong>on</strong><br />
(not all points are included to increase clarity). Also included are the mean of the DoA offset<br />
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values ß as a functi<strong>on</strong> of the MS-antenna pointing angle a, the mean of this curve, <strong>and</strong> curves indicating<br />
the range of plus minus <strong>on</strong>e st<strong>and</strong>ard deviati<strong>on</strong>. In additi<strong>on</strong>, a sinusoid curve has been fitted. The results<br />
show that there is a very small tendency of the effect indicated by Figure 9.4.<br />
Figure 9.5: Main DoA offset at base-stati<strong>on</strong> as a<br />
functi<strong>on</strong> of mobile-stati<strong>on</strong> pointing angle at<br />
Kårhuset.<br />
Figure 9.6: Main DoA offset at base-stati<strong>on</strong> as a<br />
functi<strong>on</strong> of mobile-stati<strong>on</strong> pointing angle<br />
Kårhuset.<br />
A related measure is the probability that the MS antenna pointing most directly towards the base-stati<strong>on</strong><br />
has the smallest DoA offset when received at the base-stati<strong>on</strong>. This probability is found to be around<br />
20%. These two statistics indicate that the very l<strong>on</strong>g-term DoD <strong>and</strong> DoA distributi<strong>on</strong>s may be modeled as<br />
independent. This should not be c<strong>on</strong>fused with the Kr<strong>on</strong>ecker model being valid as it operates <strong>on</strong> a<br />
shorter term. The correlati<strong>on</strong> of the DoA offset between antennas is found to be 0-50%.<br />
The angle-spread at the base-stati<strong>on</strong> was also investigated as a functi<strong>on</strong> of mobile stati<strong>on</strong> antenna pointing<br />
angle <strong>and</strong> found independent. The probability that the antenna pointing mostly towards the base stati<strong>on</strong><br />
should have the smallest angle-spread was found to be 28% at Kårhuset <strong>and</strong> 41% at Vanadis. In the<br />
Vanadis case this is a significant result. This is somewhat surprising in the light of the small (or<br />
practically n<strong>on</strong>-existing) average spread versus MS pointing angle dependence. Therefore this<br />
dependence is also not worthwhile modelling. The correlati<strong>on</strong> of angle-spreads am<strong>on</strong>g the MS antennas if<br />
found to be 26-55% at Kårhuset <strong>and</strong> 50-70% at Vanadis.<br />
9.3.2 Scenario “high mobility short range hot spot”<br />
9.3.2.1 Path-loss <strong>and</strong> shadow fading<br />
In Figure 9.7 path loss for this scenario is presented.<br />
-70<br />
-75<br />
-80<br />
PL [dB]<br />
-85<br />
-90<br />
-95<br />
-100<br />
-105<br />
-110<br />
10 1 10 2<br />
d [m]<br />
Figure 9.7: Path loss under LOS (as example <strong>on</strong>e curve out of 8 measurement runs).<br />
The table below highlights the PL exp<strong>on</strong>ents, offset K <strong>and</strong> the variance (shadow fading) within this<br />
scenario.<br />
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Under LOS c<strong>on</strong>diti<strong>on</strong> the equati<strong>on</strong> for the path loss was to be found as:<br />
PL = 60.6 + 19.3 log 10 (d), with s = 3.1 dB, (9.1)<br />
where d is the distance <strong>and</strong> s is the st<strong>and</strong>ard deviati<strong>on</strong> of the shadow fading.<br />
In Figure 9.8 distributi<strong>on</strong> of the shadow fading <strong>and</strong> SF versus distance are shown.<br />
PDF<br />
0.2<br />
0.18<br />
0.16<br />
0.14<br />
0.12<br />
0.1<br />
0.08<br />
0.06<br />
0.04<br />
0.02<br />
0<br />
-10 -8 -6 -4 -2 0 2 4 6 8 10<br />
SF [dB]<br />
(a)<br />
SF [dB]<br />
10<br />
8<br />
6<br />
4<br />
2<br />
0<br />
-2<br />
-4<br />
-6<br />
-8<br />
-10<br />
0 50 100 150 200 250<br />
d [m]<br />
(b)<br />
Figure 9.8: Shadow Fading distributi<strong>on</strong> in LOS envir<strong>on</strong>ment (a) <strong>and</strong> SF with distance (b).<br />
9.3.2.2 Modelling of PDP<br />
Power delay profile (PDP) in an outdoor high mobility short range hot spot LOS envir<strong>on</strong>ment is presented<br />
in Figure 9.9 <strong>and</strong> time c<strong>on</strong>stant in table Table 9.3.<br />
Table 9.3: Time c<strong>on</strong>stants for PDPs (MHz).<br />
Time<br />
c<strong>on</strong>stant<br />
[MHz]<br />
LOS<br />
95.9<br />
0<br />
-5<br />
Power (dB)<br />
-10<br />
-15<br />
-20<br />
-25<br />
0 10 20 30 40<br />
Excess delay [ns]<br />
(a) LOS.<br />
Figure 9.9: Modeling of power delay profile (PDP) in an outdoor high mobility short range hot spot<br />
envir<strong>on</strong>ment LOS (a) propagati<strong>on</strong> c<strong>on</strong>diti<strong>on</strong>s.<br />
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9.3.2.3 Probability of LOS<br />
In measurement data of this scenario we have probability of LOS equal to100% since the measurements<br />
were d<strong>on</strong>e <strong>on</strong> the highway without the traffic.<br />
NLOS could happen if there is a big truck in fr<strong>on</strong>t of the car (Tx). Highways always have at least 2 lines -<br />
cars are faster than trucks so there is very low probability that car is in the shadow of the truck. Also by<br />
law the distance between cars <strong>on</strong> highways should be approximately 30 m <strong>and</strong> therefore a car shouldn’t<br />
make a shadow to a car behind it if the Rx antenna is put high enough.<br />
Having this in mind probability of LOS is very high <strong>and</strong> it is lower at the higher distances. Therefore for<br />
this scenario we propose:<br />
P LOS<br />
= 1 − d *0.0004,0 < d < 250m<br />
, (9.2)<br />
where d is in meters. This functi<strong>on</strong> is presented in the figure below.<br />
1<br />
Probability of LOS<br />
0.8<br />
0.6<br />
0.4<br />
0.2<br />
0<br />
50 100 150 200 250<br />
distance [m]<br />
Figure 9.10: Probability of LOS.<br />
9.3.2.4 DS <strong>and</strong> maximum excess-delay distributi<strong>on</strong><br />
The 10, 50 <strong>and</strong> 90 % values for the Cumulative Distributi<strong>on</strong> Functi<strong>on</strong>s of the distributi<strong>on</strong> of the RMSdelay<br />
spread <strong>and</strong> maximum excess delays are given below for the 5.2 GHz centre-frequency <strong>and</strong> LOS<br />
propagati<strong>on</strong> c<strong>on</strong>diti<strong>on</strong>s for this scenario.<br />
CDF<br />
1<br />
0.9<br />
0.8<br />
0.7<br />
0.6<br />
0.5<br />
0.4<br />
0.3<br />
0.2<br />
0.1<br />
0<br />
0 10 20 30 40 50<br />
RMS delay spread [ns]<br />
CDF<br />
1<br />
0.9<br />
0.8<br />
0.7<br />
0.6<br />
0.5<br />
0.4<br />
0.3<br />
0.2<br />
0.1<br />
0<br />
0 50 100 150 200 250<br />
Max excess delay [ns]<br />
Figure 9.11: RMS delay spread, LOS<br />
Figure 9.12: Maximum excess delay, LOS<br />
Table 9.4: Percentiles RMS delay spread.<br />
Rms delay spread (ns)<br />
LOS<br />
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10% 4.4<br />
50% 4.5<br />
90% 5.8<br />
mean 5.6<br />
Table 9.5: Percentiles of maximum excess delay [ns].<br />
Maximum excess delay (ns)<br />
LOS<br />
10% 16.7<br />
50% 24.7<br />
90% 41.5<br />
mean 31.9<br />
9.3.2.5 Ricean K factors per tap<br />
The Ricean K-factor as a functi<strong>on</strong> of the distance <strong>and</strong> the CDF of it are shown in Figure 9.13. The K<br />
factor decreases slowly with increase of the distance.<br />
K factor [dB]<br />
30<br />
25<br />
20<br />
15<br />
10<br />
5<br />
0<br />
-5<br />
-10<br />
measurement based result<br />
linear fitting<br />
K [dB] = 6.4-0.001*d[m]<br />
-15<br />
0 50 100 150 200 250 300<br />
distance [m]<br />
(a)<br />
CDF<br />
1<br />
0.8<br />
0.6<br />
0.4<br />
0.2<br />
measurement<br />
based result<br />
Percentiles:<br />
10%: 1.7 dB<br />
50%: 5.9 dB<br />
90%: 11.5 dB<br />
0<br />
-20 -10 0 10 20<br />
K factor [dB]<br />
(b)<br />
Figure 9.13: Scenario B3, LOS: (a) Ricean K factor as a functi<strong>on</strong> of distance, (b) CDF of the Ricean<br />
K factor.<br />
9.3.2.6 Cross-polarizati<strong>on</strong> ratio (XPR)<br />
Prob(XPD)<br />
0.08<br />
0.07<br />
0.06<br />
0.05<br />
0.04<br />
0.03<br />
0.02<br />
0.01<br />
0<br />
-5 0 5 10<br />
XPD [dB]<br />
(a)<br />
Prob(XPD < Abscissa)<br />
1<br />
0.8<br />
0.6<br />
0.4<br />
0.2<br />
0<br />
-5 0 5 10<br />
XPD [dB]<br />
(b)<br />
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Figure 9.14: XPR 1 under LOS with (a) as PDF <strong>and</strong> (b) as CDF.<br />
Table 9.6: Percentiles of the cross-polarizati<strong>on</strong> ratio XPR 1 .<br />
Propagati<strong>on</strong><br />
c<strong>on</strong>diti<strong>on</strong><br />
Percentile<br />
(degrees)<br />
LOS<br />
NLOS<br />
10 -0.9 n.a.<br />
50 2.4 n.a.<br />
90 5.3 n.a.<br />
mean 2.4 n.a.<br />
The st<strong>and</strong>ard deviati<strong>on</strong> for the XPR 1 under LOS was found to be 2.50 dB.<br />
9.3.2.7 Azimuth AS at BS <strong>and</strong> MS<br />
The cumulative distributi<strong>on</strong> functi<strong>on</strong>s of the RMS angle-spreads at 5.20 GHz (120 MHz b<strong>and</strong>width) are<br />
shown in Figure 9.15 for LOS propagati<strong>on</strong> c<strong>on</strong>diti<strong>on</strong>s. The RMS angle-spread is calculated using the<br />
circular angle-spread formula [3GPP SCM]. No statistical fitting comparis<strong>on</strong> based <strong>on</strong> some well known<br />
techniques like KS test is applied. The percentiles for the CDF functi<strong>on</strong>s for the angle-spreads are shown<br />
in the Table 9.7 .<br />
Table 9.7: Percentiles of the RMS azimuth spread.<br />
Propagati<strong>on</strong> c<strong>on</strong>diti<strong>on</strong> LOS<br />
Link end BS MS<br />
10% 1 5<br />
Percentile (degrees) 50% 5 20<br />
90% 50 88<br />
Prob(angular spread @BS < Abscissa)<br />
1<br />
0.8<br />
0.6<br />
0.4<br />
0.2<br />
0<br />
0 20 40 60 80 100<br />
angular spread @BS [deg]<br />
(a)<br />
Prob(angular spread @MS < Abscissa)<br />
1<br />
0.8<br />
0.6<br />
0.4<br />
0.2<br />
0<br />
0 20 40 60 80 100<br />
angular spread @MS [deg]<br />
(b)<br />
Figure 9.15: RMS angle-spreads at (a) BS (AoA) <strong>and</strong> (b) MS (AoD) for the Bridge-2-Car scenario<br />
under LOS propagati<strong>on</strong> c<strong>on</strong>diti<strong>on</strong>.<br />
9.3.2.8 Distribtui<strong>on</strong> of the azimuth angles of the multipath comp<strong>on</strong>ents (AI 7.3)<br />
The cumulative distributi<strong>on</strong> functi<strong>on</strong>s of the AoAs <strong>and</strong> AoDs for the multipath comp<strong>on</strong>ents at 5.20 GHz<br />
(120 MHz b<strong>and</strong>width) are shown in Figure 9.16 Figure 9.16Figure 9.16Figure 9.16for LOS propagati<strong>on</strong><br />
c<strong>on</strong>diti<strong>on</strong>s. The percentiles for the CDF functi<strong>on</strong>s for the AoAs <strong>and</strong> AoDs are shown in the table below.<br />
Table 9.8: Percentiles of the distributi<strong>on</strong> of azimuth.<br />
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Link end BS MS<br />
Propagati<strong>on</strong><br />
c<strong>on</strong>diti<strong>on</strong><br />
Percentile<br />
(degrees)<br />
LOS NLOS LOS NLOS<br />
10 -23.6 n.a. -121.8 n.a.<br />
50 -1.8 n.a. -1.8 n.a.<br />
90 16.4 n.a. 107.3 n.a.<br />
mean -0.2 n.a. -1.8 n.a.<br />
1<br />
1<br />
Prob(angle @BS < Abscissa)<br />
0.8<br />
0.6<br />
0.4<br />
0.2<br />
Prob(angle @MS < Abscissa)<br />
0.8<br />
0.6<br />
0.4<br />
0.2<br />
0<br />
-150 -100 -50 0 50 100 150<br />
angle @BS [deg]<br />
0<br />
-150 -100 -50 0 50 100 150<br />
angle @MS [deg]<br />
(a)<br />
(b)<br />
Figure 9.16: CDFs of azimuth angles at (a) BS (AoA) <strong>and</strong> (b) MS (AoD) for the High Mobility<br />
Short Range Hot Spot – (Bridge-2-Car) scenario under LOS propagati<strong>on</strong> c<strong>on</strong>diti<strong>on</strong>.<br />
9.3.2.9 Angle proporti<strong>on</strong>ality factor<br />
The angle proporti<strong>on</strong>ality factor (r AS ) has been extracted for signals arrive at (or depart from) the MS both<br />
in LOS <strong>and</strong> NLOS propagati<strong>on</strong> c<strong>on</strong>diti<strong>on</strong>s. Figure 9.17 shows the results at the MS <strong>and</strong> BS for LOS. The<br />
percentiles for the CDF of the angle proporti<strong>on</strong>ality factor are shown in Table 9.9.<br />
1<br />
1<br />
Prob(r-factor @BS < Abscissa)<br />
0.8<br />
0.6<br />
0.4<br />
0.2<br />
Prob(r-factor @MS < Abscissa)<br />
0.8<br />
0.6<br />
0.4<br />
0.2<br />
0<br />
0 10 20 30 40<br />
r-factor @BS [deg]<br />
0<br />
0 10 20 30 40<br />
r-factor @MS [deg]<br />
(a)<br />
(b)<br />
Figure 9.17: Angle proporti<strong>on</strong>ality factor at the (a) BS <strong>and</strong> (b) MS under LOS.<br />
Table 9.9: The percentiles for the CDF of the angle proporti<strong>on</strong>ality factor at the BS <strong>and</strong> the MS,<br />
LOS.<br />
Link end BS MS<br />
Propagati<strong>on</strong><br />
c<strong>on</strong>diti<strong>on</strong><br />
LOS NLOS LOS NLOS<br />
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Percentile<br />
(degrees)<br />
10 0 n.a. 1.6 n.a.<br />
50 1.6 n.a. 4.0 n.a.<br />
90 8.1 n.a. 9.3 n.a.<br />
mean 3.7 n.a. 5.3 n.a.<br />
9.3.2.10 Number of ZDSC<br />
The results for the number of the ZDSCs shown in Figure 9.18 are calculated by resampling the data with<br />
100 MHz sampling rate. This has to be c<strong>on</strong>sidered an upper limit of the number of n<strong>on</strong>zero power delay<br />
bins. Table 9.10 presents the 10, 50 <strong>and</strong> 90 percentiles of the cumulative distributi<strong>on</strong> of the number of<br />
ZDSCs.<br />
CDF<br />
1<br />
0.9<br />
0.8<br />
0.7<br />
0.6<br />
0.5<br />
0.4<br />
0.3<br />
0.2<br />
0.1<br />
0<br />
0 2 4 6 8 10 12 14 16<br />
Number of ZDSC<br />
LOS.<br />
Figure 9.18: Number of ZDSCs.<br />
Table 9.10: CDF values for number of ZDSCs.<br />
Number of ZDSC<br />
LOS<br />
10% 3<br />
50% 4<br />
Percentile<br />
90% 5<br />
mean 4<br />
9.3.2.11 Distributi<strong>on</strong> of ZDSC delays<br />
In Figure 9.19 distributi<strong>on</strong> of the ZDSC delays for LOS envir<strong>on</strong>ment is shown<br />
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0.05<br />
0.04<br />
0.03<br />
PDF<br />
0.02<br />
0.01<br />
0<br />
0 100 200 300 400 500<br />
ZDSC delays [ns]<br />
Figure 9.19: Distributi<strong>on</strong> of the ZDSC delays.<br />
9.3.2.12 Delay proporti<strong>on</strong>ality factor<br />
Figure 9.20 shows the empirical cumulative distributi<strong>on</strong> functi<strong>on</strong> of the delay proporti<strong>on</strong>ality factor, LOS<br />
case. Percentiles of delay proporti<strong>on</strong>ality factor are given in the Table 9.11.<br />
CDF<br />
1<br />
0.9<br />
0.8<br />
0.7<br />
0.6<br />
0.5<br />
0.4<br />
0.3<br />
0.2<br />
0.1<br />
0<br />
0 1 2 3 4 5<br />
r ds<br />
(a) LOS.<br />
Figure 9.20: Delay proporti<strong>on</strong>ality factor r DS .<br />
Table 9.11: Percentiles of delay proporti<strong>on</strong>ality factor.<br />
Delay proporti<strong>on</strong>ality factor<br />
LOS<br />
10% 1.86<br />
50% 2.20<br />
Percentile<br />
90% 2.59<br />
mean 2.22<br />
9.3.2.13 Channel model tables<br />
Table 9.12: Distributi<strong>on</strong> functi<strong>on</strong>s of large-scale parameters.<br />
Bridge2Car<br />
LOS<br />
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Delayspreadσ<br />
AoD<br />
spreadσ<br />
AoA<br />
spreadσ<br />
τ<br />
φ<br />
ϕ<br />
Shadowing<br />
LN<br />
LN<br />
LN<br />
LN<br />
Table 9.13: Cross-correlati<strong>on</strong> between large-scale parameters.<br />
Bridge2Car<br />
Scenario LOS NLOS<br />
σ<br />
φ vs σ<br />
τ 0.23 n.a.<br />
Cross-Correlati<strong>on</strong>s<br />
σ<br />
ϕ vs σ<br />
τ -0.02 n.a.<br />
σ<br />
ϕ vs LNS 0.17 n.a.<br />
σ<br />
φ vs LNS 0.17 n.a.<br />
σ<br />
τ vs LNS -0.19 n.a.<br />
σ<br />
φ vs σ<br />
ϕ 0.13 n.a.<br />
Table 9.14: Additi<strong>on</strong>al parameters required for generati<strong>on</strong> of large-scale parameters.<br />
σ τ<br />
Scenarios<br />
ν<br />
(S-dB)<br />
ζ<br />
(S-dB)<br />
∆ τ<br />
Bridge2<br />
Car<br />
LOS<br />
-8.26<br />
0.2<br />
0.28<br />
σ φ<br />
(m)<br />
ν<br />
(deg-dB)<br />
ζ<br />
(deg-dB)<br />
∆ φ<br />
1.07<br />
0.31<br />
3<br />
σ ϕ<br />
(m)<br />
ν<br />
(deg-dB)<br />
ζ<br />
(deg-dB)<br />
1.24<br />
0.48<br />
∆ 4.6<br />
ϕ<br />
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LNS<br />
(m)<br />
ζ<br />
(dB)<br />
∆ LNS<br />
(m)<br />
2.3<br />
3.2<br />
Table 9.15: Lambda parameters.<br />
Bridge2Car<br />
LOS<br />
λ<br />
1 (m) 0.28<br />
λ<br />
2 (m) 3.0<br />
λ<br />
3 (m) 4.6<br />
λ<br />
4 (m) 3.2<br />
Figure 9.21: The auto correlati<strong>on</strong> functi<strong>on</strong>s obtained from (*) using the λ parameters of the table<br />
above <strong>and</strong> the single exp<strong>on</strong>ential functi<strong>on</strong>s obtained from measurements from Scenario<br />
Bridge2Car.<br />
Table 9.16: K factor formulae for LOS scenarios.<br />
Scenarios Bridge2Car<br />
K [dB] 6.4 - 0.001*d [m]<br />
Table 9.17: Distributi<strong>on</strong>s of azimuth <strong>and</strong> departure angles.<br />
Scenarios<br />
AoD<br />
distributi<strong>on</strong><br />
LOS<br />
Bridge2Car<br />
NLOS<br />
Wrapped Gaussian<br />
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AoD<br />
scaling<br />
parameter<br />
AoA<br />
distributi<strong>on</strong><br />
AoA<br />
scaling<br />
parameter<br />
5.3σ<br />
φ<br />
n.a.<br />
Wrapped Gaussian<br />
3.7σ<br />
ϕ<br />
n.a.<br />
Table 9.18: Number of ZDSCs <strong>and</strong> the number of rays in each cluster.<br />
Scenarios<br />
Bridge2Car<br />
LOS<br />
NLOS<br />
Number of ZDSC 4 n.a.<br />
rays per ZDSC 10<br />
AS<br />
φ (deg) 5.5 n.a.<br />
AS<br />
ϕ (deg) 17.8 n.a.<br />
Table 9.19: Path loss <strong>models</strong>.<br />
Scenario path loss [dB] shadow fading<br />
st<strong>and</strong>ard dev.<br />
applicability<br />
range<br />
Bridge2Car<br />
LOS 19.3 log 10 (d[m]) + 60.6 s = 3.1 dB 3m < d < 250m<br />
NLOS n.a n.a n.a<br />
Table 9.20: Scenario: LOS Clustered delay line model.<br />
ZDSC<br />
#<br />
delay<br />
[ns]<br />
Power<br />
[dB]<br />
AoD<br />
[º]<br />
AoA<br />
[º]<br />
K-<br />
factor<br />
[dB]<br />
MS speed = 1.5 km/h,<br />
directi<strong>on</strong> U(0 o ,360 o )<br />
1 0 0 4,6 -1,6 21 -0.03 * -31 **<br />
2 5 -3.1 -0,6 1,7 4 -4.56 -18.5<br />
3 10 -6.2 -4,2 2,6 -16.2<br />
4 15 -9.3 -13,0 3,6 -19.3<br />
5 20 -12.4 -17,7 6,4 -22.4<br />
6 25 -15.5 -23,7 6,5 -25.5<br />
7 30 -18.6 -35,6 3,7 -28.6<br />
8 40 -24.8 -37,1 2,4<br />
*<br />
**<br />
+<br />
- ∞<br />
Power of dominant ray,<br />
Power of each other ray<br />
Clusters with high K-factor will have 11 rays.<br />
Number of rays/ZDSC = 10<br />
Ray Power [dB]<br />
-24.8<br />
ZDSC AS at MS [º] =<br />
17.8<br />
ZDSC AS at BS [º] = 5.5<br />
Composite AS at MS [º] =<br />
5.6<br />
Composite AS at BS [º] =<br />
1.4<br />
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WINNER D5.4 v. 1.4<br />
9.4 Literature review for other scenarios<br />
9.4.1 Scenario “high mobility short range hot spot”<br />
9.4.1.1 Reference data<br />
The measurement data for the bridge to car setup were gathered with partly support by the WINNER<br />
project at a highway bridge close to Ulm (Germany). Measurement b<strong>and</strong>width <strong>and</strong> centre-frequency were<br />
selected to be 120 MHz <strong>and</strong> 5.2 GHz. The BS was mounted at the bridge (height of ~5.5m with a down<br />
tilt of 45°) whereby the transmit antennas were fixed <strong>on</strong> the roof of a car (~2m height). During the<br />
measurement LOS was dominating the propagati<strong>on</strong> characteristics, after the car went under the bridge to<br />
situati<strong>on</strong> changed to NLOS.<br />
9.4.1.2 Publicati<strong>on</strong>s<br />
Only few publicati<strong>on</strong>s to <strong>channel</strong> measurements <strong>and</strong> modelling for this scenario can be found. The spatial<br />
<strong>channel</strong> in [YTL02] for the bridge to car scenario is modelled as LOS propagati<strong>on</strong> <strong>and</strong> was used for<br />
MIMO capacity analysis.<br />
In [THL+01] first measurement results for the c<strong>on</strong>sidered scenario were published. Those data were used<br />
for the measurement based parametric <strong>channel</strong> modelling (MBPCM) approach for rec<strong>on</strong>structing <strong>channel</strong><br />
impulse resp<strong>on</strong>ses with different antenna c<strong>on</strong>figurati<strong>on</strong>s.<br />
The measurement data presented in [TSS+03] fit into the c<strong>on</strong>sidered scenario for the WINNER project.<br />
SIMO measurements with a uniform rectangle array (URA) at the receive side (bridge) were performed<br />
<strong>and</strong> are available for download. Results of super resoluti<strong>on</strong> estimati<strong>on</strong>s for angle of arrival in azimuth <strong>and</strong><br />
elevati<strong>on</strong> can be found when downloading the data. No further analysis results were published.<br />
In [TLS+05] the measured bridge to car scenario partly supported by the WINNER project was published.<br />
Analysis results in teRMS of delay window, RMS delay spread, RMS angle-spreads for transmit <strong>and</strong><br />
receive azimuth, number of paths, relative power of dense multipath comp<strong>on</strong>ents <strong>and</strong> joint probability<br />
densities between RMS delay <strong>and</strong> angle-spreads are shown. Those measurements <strong>and</strong> analysis results are<br />
part of the D5.4.<br />
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