Smart antenna based interference mitigation - Winner

WINNER II D4.7.3 v1.0
IST-4-027756 WINNER II
D4.7.3 v1.0
Smart antenna based interference mitigation
Contractual Date of Delivery to the CEC: June 30, 2007
Actual Date of Delivery to the CEC: June 30, 2007
Editor:
Magnus Olsson (EAB)
Author(s):
Mugdim Bublin, Thierry Clessienne, Eric Hardouin, Ondrej Hrdlicka,
Bernard Hunt, Geneviève Mange, Magnus Olsson, Simon Plass,
Keith Roberts, Per Skillermark, Pavol Svac, Xusheng Wei
Participant(s):
ALU, DLR, EAB, FTR&D, PRL, SAGO, SAGSK
Workpackage:
WP4 Intercell
Estimated person months: 38
Security:
PU
Nature:
R
Version: 1.0
Total number of pages: 97
Abstract: In this deliverable, smart antenna based interference mitigation methods for WINNER are
presented. These methods include beamforming techniques, transmit diversity techniques, and receive
diversity and spatial interference suppression techniques. The methods are investigated in terms of what
requirements they put on the system, and the performance they can achieve are assessed by means of
computer simulations. Based on that, recommendations for interference mitigation are given.
Keyword list:
Beamforming, Inter-cell interference, Interference mitigation, Interference suppression, Macro diversity,
MIMO, Receive diversity, SDMA, Smart antennas, Spatial-temporal processing, Transmit diversity
Disclaimer:
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WINNER II D4.7.3 v1.0
Executive summary
The WINNER radio interface is designed to provide users with high quality of service wireless access. It
should further be configurable to a wide range of scenarios, be spectrally efficient and allow for a costefficient
deployment. One possible means to improve the spectrum efficiency and limit the deployment
cost is to create a radio interface that is inherently robust to interference. With a high tolerable
interference level the load in the radio network may be increased without degrading the user’s quality of
service. That is, more users may be served in the same spectrum.
In general, radio communication links may experience several kinds of interference like inter-symbol
interference (ISI), multiple access interference (MAI) and interference from external sources. ISI may be
caused by a time dispersive radio channel while MAI appears when the limited set of radio resources is
re-used throughout the system. In order to reach high spectrum efficiency, it is important that the radio
interface in particular can handle MAI. The MAI can originate either from the own cell, so called intracell
interference, or from other cells, so called inter-cell interference. Since the multiple access schemes
in WINNER are designed to be orthogonal within a cell, there will be no, or very little, intra-cell
interference. In other words, the MAI in a WINNER network mainly comprises inter-cell interference.
There are different methods available to mitigate inter-cell interference. Interference averaging
techniques aim at average the interference over all users, thereby reducing the interference experienced by
some users. Interference avoidance techniques on the other hand, aim at explicitly coordinate and avoid
interference, e.g. by setting restrictions on how the radio resources are used. However, in this deliverable
it has been investigated how smart antennas can be used to mitigate inter-cell interference.
The starting point for the work was the WINNER multiple antenna concept, and it was first investigated
how it can be utilised for interference mitigation. The identified methods can be divided in three
categories: beamforming techniques, transmit diversity techniques, and receive diversity / interference
suppression techniques. These methods were then investigated in terms of what requirements they put on
the system concept regarding e.g. architecture, measurements and signalling, and assessed from a
performance point of view. The performance assessments were mainly carried out via computer
simulations. Most of the simulations were performed on system level, but some aspects were also studied
by link level and multi-link simulations.
The conclusion is that it is possible to significantly improve the robustness to inter-cell interference by
using smart antennas. Transmit beamforming, for example in the form of fixed Grid of Beams (GoB), is
an efficient means to reduce the interference spread in the system, and in particular to protect users at the
cell border from inter-cell interference. With SDMA on top of GoB, the system performance is improved
at the expense of slightly less protection of the cell edge users. Also the use of multi antenna receivers at
both base stations and user terminals has been shown to be efficient means to mitigate interference. This
allows implementation of receive diversity combining schemes such as Maximum Ratio Combining
(MRC) and spatial interference suppression schemes such as Interference Rejection Combining (IRC).
Already MRC provides considerable improvements in interference tolerance both when used at user
terminals in downlink and at base stations in uplink. Additional improvements are achieved with IRC, in
particular for downlink reception at user terminals. In this context it should be mentioned that IRC is a
more complex method than MRC, but studies on how this complexity can be reduced thereby saving e.g.
terminal battery life have also been conducted. For downlink, the combination of transmit beamforming
at the base stations and multi antenna reception with IRC at user terminals has been identified as an
attractive combination. Furthermore, different aspects of macro diversity, i.e. transmit diversity from
several base stations, have been studied. For example, different receive combining methods for Single
Frequency Networks (SFN) and Multiple Frequency Networks (MFN) in conjunction with MBMS were
investigated. In addition, one macro diversity method based on cyclic delay diversity (CDD) was shown
to have potential to further improve the inter-cell interference situation at cell border areas.
Some work has also been spent on how the smart antenna based interference mitigation methods can be
combined with interference averaging and interference avoidance methods. However, these studies have
considered only some averaging and avoidance methods. Therefore, further work is needed in this area in
order to come up with a complete and adaptive inter-cell interference mitigation strategy for the
WINNER system. This will be the focus of the work on interference mitigation during the remainder of
WINNER phase II.
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WINNER II D4.7.3 v1.0
Authors
Partner Name Phone / Fax / e-mail
Alcatel-Lucent Germany (ALU)
Geneviève Mange Phone: +49 711 82141407
Fax: +49 711 82132300
e-mail: genevieve.mange@alcatel-lucent.de
Ericsson AB (EAB)
Magnus Olsson Phone: +46 8 585 30774
Fax: +46 8 585 31480
e-mail: magnus.a.olsson@ericsson.com
Per Skillermark Phone: +46 8 585 31922
Fax: +46 8 757 57 20
e-mail: per.skillermark@ericsson.com
France Telecom (FTR&D)
German Aerospace Center (DLR)
Philips Electronics UK Ltd (PRL)
Siemens AG Austria (SAGO)
Siemens AG Slovakia (SAGSK)
Thierry Clessienne Phone: +33 1 45 29 48 80
Fax: +33 1 45 29 41 94
e-mail: thierry.clessienne@orange-ftgroup.com
Eric Hardouin Phone: +33 1 45 29 44 16
Fax: +33 1 45 29 41 94
e-mail: eric.hardouin@orange-ftgroup.com
Simon Plass Phone: +49 8153 282874
Fax: +49 8153 281871
e-mail: simon.plass@dlr.de
Bernard Hunt Phone: +44 1293 815055
Fax: +44 1293 815024
e-mail: bernard.hunt@philips.com
Keith Roberts No longer with PRL
Xusheng Wei No longer with PRL
Mugdim Bublin Phone: +43 51707 21724
Fax: +43 51707 51933
e-mail: mugdim.bublin@siemens.com
Ondrej Hrdlicka Phone: +421 2 5968 4639
Fax: +421 2 5968 5409
e-mail: ondrej.hrdlicka@siemens.commailto:
Pavol Svac Phone: +421 2 5968 4663
Fax: +421 2 5968 5409
e-mail: pavol.svac@siemens.com
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WINNER II D4.7.3 v1.0
Table of contents
List of acronyms and abbreviations ............................................................... 7
1. Introduction ................................................................................................. 9
1.1 Background ................................................................................................................................. 9
1.2 Scope ......................................................................................................................................... 10
1.3 Outline....................................................................................................................................... 10
2. Overview on smart antenna based interference mitigation .................. 11
2.1 The WINNER multiple antenna concept................................................................................... 11
2.2 Smart antenna based interference mitigation techniques........................................................... 12
2.2.1 Beamforming .................................................................................................................... 12
2.2.2 Transmit diversity ............................................................................................................. 13
2.2.3 Receive diversity and interference suppression ................................................................ 13
2.2.4 Combinations of methods ................................................................................................. 13
2.3 Impact on system architecture, measurements, and signalling .................................................. 14
3. Smart antenna based interference mitigation methods ........................ 16
3.1 Beamforming techniques........................................................................................................... 16
3.1.1 Adaptive beamforming...................................................................................................... 16
3.1.2 Fixed beamforming or Grid of Beams (GoB) ................................................................... 16
3.1.3 Fixed beam design and scheduling for GoB SDMA......................................................... 18
3.2 Transmit diversity techniques.................................................................................................... 19
3.2.1 Closed loop transmit diversity........................................................................................... 19
3.2.2 Cellular Cyclic Delay Diversity (C-CDD) ........................................................................ 20
3.2.3 Macro diversity for MBMS............................................................................................... 21
3.3 Receive diversity and interference suppression techniques....................................................... 21
3.3.1 Maximum Ratio Combining (MRC) ................................................................................. 21
3.3.2 Interference Rejection Combining (IRC).......................................................................... 22
4. Assessments............................................................................................. 24
4.1 Methodology, assumptions, and assessment criteria ................................................................. 24
4.1.1 Inter-cell interference modelling.......................................................................................24
4.1.1.1 Link level inter-cell interference modelling........................................................... 25
4.1.1.2 System level inter-cell interference modelling ...................................................... 25
4.1.2 Assumptions...................................................................................................................... 26
4.1.3 Assessment criteria ........................................................................................................... 27
4.2 Results ....................................................................................................................................... 28
4.2.1 Downlink interference mitigation with multiple antennas ................................................ 28
4.2.1.1 Non-frequency adaptive transmissions .................................................................. 28
4.2.1.2 Impact of frequency adaptivity .............................................................................. 29
4.2.1.3 Impact of SDMA ................................................................................................... 30
4.2.1.4 Interplay between beamforming, scheduling, and channel allocation strategies ... 32
4.2.1.5 Complexity reduction of IRC weight calculation at UT receiver .......................... 32
4.2.2 Uplink interference mitigation with multiple antennas..................................................... 33
4.2.3 Macro diversity ................................................................................................................. 35
4.2.3.1 Link level investigations of C-CDD based macro diversity .................................. 35
4.2.3.2 Evaluations of macro diversity techniques for MBMS.......................................... 36
5. Recommendations for interference mitigation....................................... 37
6. Conclusions .............................................................................................. 39
7. References................................................................................................. 40
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WINNER II D4.7.3 v1.0
Appendix A. Inter-cell interference modelling........................................ 42
A.1 Scenarios ................................................................................................................................... 42
A.2 Results............................................................................................................................... 43
A.2.1 Number of significant links ................................................................................... 43
A.2.2 Identification of significant links ........................................................................... 44
Appendix B. Downlink interference mitigation with multiple antennas in
non-frequency adaptive networks........................................................... 46
B.1 Description ................................................................................................................................ 46
B.2 Scenarios ................................................................................................................................... 46
B.3 Requirements............................................................................................................................. 46
B.4 Evaluations ................................................................................................................................ 47
B.4.1 Assumptions...................................................................................................................... 47
B.4.2 Results............................................................................................................................... 48
Appendix C. Downlink interference mitigation with multiple antennas in
frequency-adaptive and non-adaptive networks.................................... 51
C.1 Description of the considered mitigation techniques................................................................. 51
C.1.1 Maximum Ratio Combining (MRC) ................................................................................. 51
C.1.2 Interference Rejection Combining (IRC).......................................................................... 51
C.2 Requirements............................................................................................................................. 52
C.3 Evaluations ................................................................................................................................ 52
C.3.1 Assumptions...................................................................................................................... 52
C.3.2 Performance results........................................................................................................... 53
C.4 Conclusions ............................................................................................................................... 56
Appendix D. Downlink interference mitigation with multiple antennas
and an SDMA scheme............................................................................... 57
D.1 Description ................................................................................................................................ 57
D.1.1 Closed loop transmit diversity........................................................................................... 57
D.1.2 Fixed Grid of Beams (GoB).............................................................................................. 58
D.1.3 Fixed beam design and scheduling for GoB SDMA......................................................... 59
D.2 Scenarios ................................................................................................................................... 60
D.3 Evaluations ................................................................................................................................ 60
D.4 Results ....................................................................................................................................... 60
Appendix E. Interplay between smart antennas and other interference
mitigation techniques in downlink .......................................................... 64
E.1 Simulation assumptions and modelling ..................................................................................... 64
E.2 Simulation results ...................................................................................................................... 65
E.3 Conclusions ............................................................................................................................... 67
Appendix F. Complexity reduction of interference mitigation at UT
receiver using multiple antennas ............................................................ 68
F.1 Description ................................................................................................................................ 68
F.2 Scenarios ................................................................................................................................... 71
F.3 Requirements............................................................................................................................. 72
F.4 Evaluations ................................................................................................................................ 72
F.4.1 Assumptions...................................................................................................................... 72
F.4.2 Results............................................................................................................................... 72
Appendix G. Uplink interference mitigation with multiple antennas.... 74
G.1 Description ................................................................................................................................ 74
G.2 Scenarios ................................................................................................................................... 74
G.3 Requirements............................................................................................................................. 74
G.4 Evaluations ................................................................................................................................ 74
G.4.1 Assumptions...................................................................................................................... 74
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G.4.2 Results............................................................................................................................... 75
Appendix H. Macro diversity in the form of Cellular Cyclic Delay
Diversity (C-CDD)...................................................................................... 80
H.1 Description ................................................................................................................................ 80
H.2 Scenarios ................................................................................................................................... 80
H.3 Requirements............................................................................................................................. 81
H.4 Evaluations ................................................................................................................................ 81
H.4.1 Assumptions...................................................................................................................... 81
H.4.2 Results............................................................................................................................... 81
Appendix I. Macro diversity techniques for MBMS .............................. 84
I.1 Description ................................................................................................................................ 84
I.1.1 MBMS............................................................................................................................... 84
I.1.2 Inter-cell interference mitigation based on macro diversity.............................................. 84
I.2 Requirements............................................................................................................................. 85
I.3 Evaluations ................................................................................................................................ 85
I.3.1 Transport channel multiplexing structure for MBMS....................................................... 85
I.3.2 Combining algorithms for multiple frequency networks ..................................................87
I.3.2.1 Selective combining............................................................................................... 87
I.3.2.2 Soft combining....................................................................................................... 87
I.3.3 Assumptions...................................................................................................................... 87
I.3.4 Results............................................................................................................................... 88
I.3.4.1 Multiple Frequency Network (MFN)..................................................................... 88
I.3.4.2 Single Frequency Network (SFN) ......................................................................... 92
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List of acronyms and abbreviations
AWGN Additive White Gaussian Noise
B-EFDMA Block-Equidistant Frequency Division Multiple Access
B-IFDMA Block-Interleaved Frequency Division Multiple Access
BCH
Broadcast Channel
BER
Bit Error Rate
BLER
Block Error Rate
BS
Base Station
C/I
Carrier to Interference Ratio
C-CDD Cellular Cyclic Delay Diversity
CDD
Cyclic Delay Diversity
CDF
Cumulative Distribution Function
CHAN INP Channel Interpolation
CQI
Channel Quality Indicator
CRC
Cyclic Redundancy Check
CSI
Channel State Information
DCA
Dynamic Channel Allocation
DFT
Discrete Fourier Transform
DL
Downlink
DoA
Direction of Arrival
E2E
End-to-End
EMBMS Evolved Multimedia Broadcast Multicast Service
EQUZ INP Equaliser Interpolation
FBI
Feedback Indicator
FDD
Frequency Division Duplex
FDE
Frequency Domain Equalisation
FDM
Frequency Division Multiplexing
FDMA
Frequency Division Multiple Access
FEC
Forward Error Correction
FET
Frequency Expanding Technique
FFT
Fast Fourier Transform
FTP
File Transfer Protocol
GMC
Generalised Multi Carrier
GoB
Grid of Beams
GSM
Global System for Mobile communications
HARQ
Hybrid Automatic Repeat Request
HPBW
Half Power Bandwidth
IFFT
Inverse Fast Fourier Transform
IRC
Interference Rejection Combining
ISI
Inter Symbol Interference
LA
Local Area
LDC
Linear Dispersion Code
LDPC
Low-Density Parity-Check (codes)
LOS
Line Of Sight
LP
Linear Precoding
LS
Least Square
MA
Metropolitan Area
MAC
Medium Access Control
MAI
Multiple Access Interference
MBMS Multimedia Broadcast Multicast Service
MC-CDMA Multicarrier Code Division Multiple Access
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MCS
MFN
MIMO
MMSE
MRC
OFDM
OFDMA
PCE
PDU
PHY
PL
QoS
RCE
RF
RL
RLC
RRM
RU
Rx
SA
SAP
SDMA
SFN
SINR
SISO
SNR
STBC
TDD
TDM
TDMA
Tx
UL
ULA
UT
WA
Modulation and Coding Scheme
Multiple Frequency Network
Multiple-Input Multiple-Output
Minimum Mean Square Error
Maximum Ratio Combining
Orthogonal Frequency Division Multiplexing
Orthogonal Frequency Division Multiple Access
Perfect Channel Estimation
Protocol Data Unit
Physical Layer
Path-Loss
Quality of Service
Realistic Channel Estimation
Radio Frequency
Resource Load
Radio Link Control
Radio Resource Management
Resource Unit
Receive
Smart Antennas
Service Access Point
Spatial Division Multiple Access
Single Frequency Network
Signal to Interference plus Noise Ratio
Single-Input Single-Output
Signal to Noise Ratio
Space Time Block Codes
Time Division Duplex
Time Division Multiplexing
Time Division Multiple Access
Transmit
Uplink
Uniform Linear Array
User Terminal
Wide Area
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WINNER II D4.7.3 v1.0
1. Introduction
1.1 Background
The goal of WINNER is to develop a single ubiquitous radio access system adaptable to a comprehensive
range of mobile communication scenarios. This will be based on a single radio access technology with
enhanced capabilities compared to existing systems or their evolutions in order to provide users with high
quality of service wireless access. The radio interface should further be spectrally efficient and allow for a
cost-efficient deployment. One possible means to improve the spectrum efficiency and limit the
deployment cost is to create a radio interface that is inherently robust to interference. With a high
tolerable interference level the load in the radio network may be increased without degrading the user’s
quality of service. That is, more users may be served in the same spectrum.
In general, radio communication links may experience several kinds of interference like inter-symbol
interference (ISI), multiple access interference (MAI) and interference from external sources. ISI may be
caused by a time dispersive radio channel while MAI appears when the limited set of radio resources is
re-used throughout the system. In order to reach high spectrum efficiency, it is important that the radio
interface in particular can handle MAI. The MAI can originate either from the own cell, so called intracell
interference, or from other cells, so called inter-cell interference.
The current WINNER design is based on (standard or pre-coded) OFDM transmission in both downlink
and uplink. The basic resource unit is denoted a chunk and comprises a set of adjacent sub-carriers and
(time) symbols. Such a chunk is the smallest unit that can be scheduled for transmission. Spatial
processing (e.g. by SDMA or spatial multiplexing) allows re-use of each chunk, via so-called chunk
layers. A frequency-adaptive transmission mode co-exists with a non-frequency adaptive mode. In the
frequency-adaptive mode channel dependent scheduling provides multi-user diversity and the
transmissions are adapted according to the short term channel fading and interference fluctuations.
Adaptation may be performed in the time, the frequency and the spatial domains. In the non-frequency
adaptive mode only the long term channel and interference variations are accounted for. For multiple
access the frequency-adaptive mode relies on chunk-based OFDMA/TDMA in downlink as well as in
uplink. In downlink, the non-frequency adaptive transmission mode uses B-EFDMA while the
corresponding uplink mode is called B-IFDMA, which is a form of pre-coded OFDM transmission using
an equidistant sub-carrier assignment. Furthermore, any of the multiple access schemes may be combined
with an additional SDMA component. More details on the multiple access schemes can be found in
[WIN2D461].
In both uplink and downlink the multiple access schemes, except SDMA, are designed to be orthogonal
within a cell. That is, in the ideal case a user experiences no, or very little, intra-cell interference. In other
words, the MAI in a WINNER network mainly comprises inter-cell interference.
There are different methods available to mitigate inter-cell interference. Interference averaging
techniques [WIN2D471] aim at average the interference over all users, thereby reducing the interference
experienced by some users. Interference avoidance techniques [WIN2D472] on the other hand, aim at
explicitly coordinate and avoid interference, e.g. by setting restrictions on how the radio resources are
used.
Another way to mitigate the negative effects of inter-cell interference is to utilise smart antennas at the
transmitters and the receivers in the system, which will be the focus of this deliverable. Within WINNER,
much work has already been carried out in the area of smart antennas, and a unified and generic multiple
antenna concept has been developed. It allows utilisation of the spatial domain for adaptive directivity,
diversity and multiplexing. Further details of the work on the multiple antenna concept can be found in
previous WINNER deliverables, e.g. [WIN1D27], [WIN1D210], and [WIN2D341].
When it comes to mitigation of inter-cell interference, in particular directivity and diversity are important.
Directivity can be used to reduce the interference spread to other cells in a cellular system, and diversity
can be used to mitigate the deteriorating effects of fading, which in turn allows reduction of the transmit
power of the radio transmitters in the system. Furthermore, diversity in terms of multiple receive antennas
is also important as basis for implementation of spatial interference suppression schemes in the radio
receivers.
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1.2 Scope
In this deliverable, we will investigate different smart antenna based methods to mitigate inter-cell
interference. These include transmit beamforming, transmit diversity at the cell border (also known as
macro diversity), and receive diversity as well as spatial interference suppression. The focus will be on
achievable performance, but also complexity aspects as well as the requirements the techniques put on the
system concept will be considered.
1.3 Outline
The outline of the report is as follows: First, an overview of different smart antenna based inter-cell
interference mitigation methods are given in Chapter 2. Then these methods are described in more detail
in Chapter 3, both in terms of how they work and what requirements they put on the system. After that we
assess the methods both from a performance and a complexity point of view, and try to identify suitable
combinations of methods, also at least to some extent considering averaging and avoidance methods, in
Chapter 4 and 5. Finally, conclusions are drawn in Chapter 6. In addition, details on the methods and in
particular the assessments can be found in appendices.
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WINNER II D4.7.3 v1.0
2. Overview on smart antenna based interference mitigation
As was mentioned in the introduction chapter above, one way to mitigate inter-cell interference is the use
of smart antennas at the transmitters and/or receivers in a wireless communication system. In this context
we define a smart antenna as an antenna arrangement consisting of multiple antenna elements, that can
be co-located or distributed, where the transmission and/or reception is controlled by advanced signal
processing functionality. As was also mentioned in the previous chapter, work on smart antennas has
already to large extent been carried out in WINNER, and a multiple antenna concept has been developed.
In the following, this concept and how it can be used to mitigate inter-cell interference will be described.
2.1 The WINNER multiple antenna concept
The WINNER multiple antenna concept is a generic and flexible MIMO transmission concept based on
per stream rate control, linear dispersion codes and linear precoding [WIN2D341], which allows flexible
combinations of directivity, diversity, and multiplexing to be realised in an adaptive manner. The main
idea behind the WINNER multiple antenna concept is to adapt the transmission to the user needs in
different deployments under different conditions of channel and interference knowledge obtained by
measurements, feedback and exploitation of reciprocity.
Figure 2-1 shows a block diagram of the generic spatial transmit processing. As mentioned in Chapter 1,
the basic physical transmission unit is called a chunk and consists of several adjacent sub-carriers of few
consecutive OFDM symbols. The chunk size is chosen so that the channel variations within a chunk are
negligible, see [WIN2D6137] for more details.
Figure 2-1: Generic spatial transmit processing.
At the input of the transmitter are the incoming data transport blocks from higher layers. Each of these
transport blocks is segmented and channel encoded in a forward error correction (FEC) entity. These
encoded segments of transport blocks are referred to as FEC blocks. An important property of the radio
interface is that each chunk is partitioned into one or several spatial layers denoted as chunk layers. The
above described FEC blocks, i.e. encoded segments of transport blocks, are multiplexed onto these layers.
The bits mapped to each chunk layer are separately modulated. The so formed modulated chunk layers
are then dispersed or spread onto virtual antenna chunks with a linear dispersion code which is a three
dimensional entity spanning the adjacent sub-carriers of the consecutive OFDM symbols in time and
frequency corresponding to the chunk in addition to the spatial dimension which has been added. All
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WINNER II D4.7.3 v1.0
virtual antenna chunks are then subject to a processing technique that is referred to as generalised multicarrier
(GMC) processing. The GMC function operates on an OFDM symbol basis in the frequency
domain over all chunks allocated to a transport block. More specifically, the layers of virtual antenna
chunks are jointly processed by an identity function (when OFDM is used) or a Discrete Fourier
Transform (DFT) function (in the case of pre-coded OFDM) and then split and dispersed over the virtual
antenna chunks again. The virtual antenna chunk of each layer is further subject to linear precoding.
Finally, the layers’ antenna chunks are summed over the antennas to form a three-dimensional antenna
chunk, which is passed to assembly and OFDM modulation per antenna.
Depending on the scenario, system load, propagation conditions, and number of receivers (unicast,
multicast or broadcast), varying spatial processing gains (multiplexing, diversity, and directivity) will be
exploited to different degrees and therefore different spatial schemes will be applied. In a particular
spatial scheme not all of the above function blocks will be operational. Thus, arbitrary combinations of
the function blocks for segmentation, linear dispersion coding, and linear precoding may be used.
What has been written so far is focused on the transmit processing. When it comes to receive processing,
most of the spatial-temporal transmit processing techniques can be received with standard receiver
structures [WIN1D27]. In order to efficiently exploit the capabilities and the flexibility of the generic
transmitter the corresponding receivers must typically be equipped with several receive antennas.
Multiple receive antennas are e.g. required for efficient spatial multiplexing and are also important in
order to improve the diversity and the robustness to interference since it allows implementation of
interference suppression techniques in the receive processing.
2.2 Smart antenna based interference mitigation techniques
There are several ways to use smart antennas in a wireless communication system in order to mitigate
inter-cell interference. They typically utilise the directivity and/or diversity properties of the spatial
processing that can be achieved with the multiple antenna concept described above. In the following the
families of smart antenna based interference mitigation techniques will be introduced and the main ideas
behind them briefly described. More detailed descriptions will then be given in Chapter 3.
2.2.1 Beamforming
The use of different beamforming techniques enables transmission in narrow beams in certain directions
thereby reducing the interference spread in the system, see Figure 2-2. The beamforming can either be
fixed or adaptive. In fixed beamforming, also called Grid of Beams (GoB), the transmitter has a certain
number of pre-formed beams among it can select the best beam for transmission, while in adaptive
beamforming the antenna weights are set adaptively based on the channel knowledge which allows
optimisation of the antenna pattern according to different criteria. Beamforming can also be used for
reception where it is possible to direct the receiving beam in the direction of the desired transmission.
When adaptive beamforming is used it is also possible to put nulls in certain directions, e.g. where strong
interferers are located. See also Section 2.2.3 below. Transmit beamforming is in principle applicable
both at BS and UT, but in practice it will most probably be limited to the BS.
Figure 2-2: Transmit beamforming from base station.
Beamforming techniques require channel knowledge in order to adapt/select the beam pattern. The
amount of channel knowledge varies depending on technique; adaptive techniques may require rather
complete channel state information (CSI) to be reported, while other techniques just require channel
quality indicators (CQI), and some techniques can utilise uplink information to select the most suitable
beam and thereby require no information to be reported. More details on this can be found in
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WINNER II D4.7.3 v1.0
[WIN2D341]. In case the beamforming in different cells is to be coordinated, additional inter-BS
signalling might be required.
2.2.2 Transmit diversity
By transmitting the same signal from different antennas it is possible to improve the reception in the other
end of the link due to enhanced diversity. This allows reduction of the transmit power and thereby the
interference spread in the system. This is in particular useful at cell borders where the inter-cell
interference level typically is high, and several BSs can cooperate (so called macro diversity), see Figure
2-3.
Figure 2-3: Macro diversity.
Transmit diversity as such, e.g. so called open-loop transmit diversity, has no specific requirements on
measurements and signalling. However, more advanced schemes such as so called closed loop transmit
diversity require channel knowledge to be reported. When macro diversity is applied, i.e. the case where
two or several BSs cooperate, additional inter-BS signalling and time synchronisation is required.
2.2.3 Receive diversity and interference suppression
The use of multiple antennas at receivers facilitates establishment of spatial diversity branches, which can
be used for implementation of receive diversity and/or interference suppression techniques in the receive
processing. A simple illustration of this is shown in Figure 2-4. The most well-known method for receive
diversity is traditional Maximum Ratio Combining (MRC) where the combining weights are selected
accounting for the radio channel (of the desired signal), the noise power and the interference power at the
different receive antennas. Hence, MRC can be seen as receive beamforming. With interference
suppression techniques, also the spatial or spatial-temporal properties of the interference are taken into
account. One example is Interference Rejection Combining (IRC), a.k.a. Minimum Mean Square Error
(MMSE) or optimal combining [Win84], which determines the combining weights based on the channel
and the (spatial) noise and interference covariance matrix, i.e., not only the interference power but also
the spatial colouring of the interference is considered. This means that IRC put nulls in the direction of
the interferer(s). Receive diversity and interference suppression is applicable both at the BS and the UT.
Figure 2-4: Receive diversity at base station.
Interference suppression techniques require accurate interference measurements, which put requirements
on the pilot design. In addition, it is advantageous with a time synchronised network, but it is not a
requirement; the interference suppression techniques will still work but the gain will not be maximised.
2.2.4 Combinations of methods
The smart antenna based interference mitigation methods introduced above can also be combined with
each other, as well as with interference averaging and interference avoidance methods. One example is
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the combination of transmit beamforming at the BS and the use of interference suppressing UTs. These
methods have earlier been demonstrated to complement each other very well and provide almost additive
gains, e.g. for GSM in [CBB+01]. The reason for this is that most baseband interference suppression
methods, e.g. IRC, are designed to be very efficient for cases with one or few strongly dominating
interfering sources, and that beamforming, i.e. to transmit in narrow beams in certain directions, creates
an interference environment that very often is characterised by this.
Most of the smart antenna based interference mitigation methods can in principle be used in conjunction
also with interference averaging [WIN2D471] and interference avoidance [WIN2D472] methods, and
provide additional gains. For example, UT receivers with interference suppression capabilities will work
in any network and provide additional robustness to interference, even though the level of interference
might be lower due to e.g. an avoidance scheme.
2.3 Impact on system architecture, measurements, and signalling
The inter-cell interference mitigation techniques put some requirements on the system concept. This
could be in the form of architecture (e.g. a centralised node for RRM), measurements (feedback of
channel knowledge, etc.) and signalling (e.g. communication between BSs). When it comes to smart
antenna based interference mitigation techniques, these requirements are limited. Some of them have
already been indicated in the previous sections, however, in the following we will discuss this in
somewhat more detail.
Figure 2-5 below shows how the generic transmit processing, which was described in Section 2.1 above,
is related to the radio interface structure. The generic transmitter is implemented at the PHY layer, and the
configuration of it is controlled from the MAC layer. The data units are handed from the transmitting
MAC entity through PHY service access point (SAP) to the transmitting PHY entity and the generic
transmitter is configured by the same transmitting MAC entity through a separate control SAP. A reverse
operation of transmitter functions needs to be performed at the receiver before data units can be delivered
from the receiving PHY entity to the receiving MAC entity through SAP. The receiver should therefore
have detailed knowledge about the transmitter configuration, e.g. for demodulation and decoding
purposes. Therefore, transmit control signalling is needed. Similarly, the generic transmitter might require
measurements to be reported from the receiver, e.g. for beamforming purposes as described above in
Section 2.2.1. However, all this is already integrated in the system concept; more details can be found in
[WIN2D341].
MAC Protocol Data Units (PDUs)
MAC layer
PHY Control SAP
PHY Service Access Point (SAP)
PHY layer
transport blocks
transport blocks
transport blocks
receiver
transmitter
segmentation
segmentation
segmentation
FEC
FEC
FEC
FEC FEC FEC
FEC FEC FEC
MUX
MOD
MOD
MOD
MOD
LDC
LDC
LDC
LDC
GMC
LP
LP
Antenna summation
Assembly of chunks to raw symbol data
IFFT
CP
Figure 2-5: Generic transmitter in relation to radio interface structure.
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Hence, the smart antenna based inter-cell interference mitigation methods have no major impact on the
system architecture. They are mainly implemented at the PHY layer, and controlled from the MAC layer.
The measurements needed for e.g. beamforming purposes are already defined for the multiple antenna
concept [WIN2D341], and no further requirements are added from an inter-cell interference mitigation
point of view. The only exceptions are the macro diversity techniques, where the same signals are
transmitted from several BSs, hence these transmissions need to be coordinated somehow, e.g. via inter-
BS signalling. This is also the case if beamforming in different cells is to be coordinated, however, no
such techniques will be considered in this deliverable.
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3. Smart antenna based interference mitigation methods
The use of smart antennas for mitigating inter-cell interference in the context of WINNER has been
introduced in the previous chapter, as well as the different families of techniques investigated. These
families are beamforming techniques and diversity techniques both for transmission and reception. This
view includes the use of these methods for both downlink and uplink meaning respectively BS(s) to
UT(s) communication and UT(s) to BS(s) communication. In this chapter we will provide more detailed
descriptions of the different techniques considered, along with the requirements they put on the system
concept in terms of architecture, measurements, signalling, etc.
3.1 Beamforming techniques
Beamforming is an efficient means to combat inter-cell interference, and in particular to protect the user
at the cell border. By transmitting in a narrow beam directed towards the desired user instead of a sectorwide
beam, it is possible to significantly reduce the interference spread to other cells in the system.
Beamforming can be carried out in different ways; at the highest level we distinguish adaptive and fixed
beamforming. In adaptive beamforming the antenna weights are set in order to optimise the antenna
pattern. This can be done according to several different optimisation criteria and based on different
amounts of channel knowledge [WIN2D341]. In fixed beamforming, or GoB approaches, a certain
number of pre-defined beams are used, and the beamforming problem reduces to beam selection, which
requires less feedback information than adaptive approaches. For both adaptive and fixed beamforming
correlated antennas are preferred, e.g. with half a wavelength element separation. Transmit beamforming
is in principle applicable both at BS and UT, but in practice it will most probably be limited to the BS.
It is also possible to implement SDMA as further multiple access component on top of beamforming in
order to serve several users at the same time on the same resources, but spatially separated. With
appropriate scheduling this allows significant improvements in system performance [WIN2D341].
However, for users at the cell border it might be a disadvantage due to the increased interference in the
system.
In the following we will focus on a traditional adaptive beamforming scheme, a simple single stream GoB
scheme, and an SDMA variant built on GoB optimised with so called beam tapering.
3.1.1 Adaptive beamforming
In adaptive beamforming the antenna pattern is adapted in order to provide optimal reception for the
scheduled user. This optimisation can be done according to different criteria. The adaptation is based on
channel knowledge, typically long term CSI in the form of a spatial transmit covariance matrix. In the
following we will briefly describe how adaptive beamforming can be achieved. Note that there exist
several other versions and optimisations as well, details of some of them can be found in e.g. [WIN1D27].
We consider the downlink of a wireless communication system. Beamforming in a MIMO channel H
means that an input signal s is transmitted with the power p and the antenna weight vector v, resulting in
the receive vector y in the presence of interference plus noise z, according to:
y
{
= H{ {
v
{
p s
{
+ z
{
M × 1 M R × M T M × 1 1×
1 1×
1 M × 1
R
T
The antenna weight vector, or the beamforming vector, v, is set according to some method. One method is
eigenbeamforming [HBD00], where the beamforming vector is set to the eigenvector corresponding to
the largest eigenvalue of the spatial transmit covariance matrix.
R
3.1.2 Fixed beamforming or Grid of Beams (GoB)
Adaptive beamforming as described in the previous section adapts the transmit weights for each user to
long term CSI at the transmitter such as the transmit covariance matrix. A low-complex approach to such
adaptive beamforming is to use a finite set of fixed antenna weights, which will generate a set of beams
matched to the long term transmit covariance matrices of different parts of the coverage area. All users in
the coverage area share the set of beams, and the problem of beamforming reduces to beam selection. The
amount of channel knowledge required at the transmitter, once the beams have been designed, is small
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and since the adaptation is typically done to long term CSI, the required amount of feedback signalling is
low or none in case that the uplink received signals are used to determine which beam is the best.
Here we will describe a basic fixed beamforming scheme, in WINNER referred to as Grid of Beams
(GoB). It is in [WIN2D6137] defined as the baseline spatial processing for the wide area scenario, and is
intended to be used for single stream transmission (i.e. no SDMA) in a triple sectored site.
For transmission to a certain user the beamforming vector v is chosen from a predefined static set of
complex numbered antenna weights, so-called beams. The receive vector of each user and sub-carrier has
the following form,
y
{
= H{ {
v
{
p s
{
+ z
{
where v ∈ { v v ... }
M × 1 M R × M T M × 1 1×
1 1×
1 M × 1
R
T
1 2
v I , and I denotes the number of available beams. H is the MIMO
channel matrix, p the transmit power, s the transmitted symbol and z the vector of interference plus noise.
The simplest beam selection is achieved by choosing each user’s best beam on a long-term basis over the
whole frequency band. This requires a minimal feedback rate. It is suggested to calculate, for the user
terminal, the time-frequency averaged receive power per beam i (in reality e.g. obtained by combining
common pilots with the different beam weights in the UT)
P i
=∑∑∑
n k s
h
2
n, k , s vi
over all indexes n of receive antennas, k of sub-carriers and s of symbols, with h n,k,s being the n-th row
vector of H (the channel from all transmit antennas to a certain receive antenna n) for a certain symbol
and sub-carrier. To reduce the number of calculations this can be sub-sampled e.g. on a chunk grid. For
UT, the beam index i for the beam with the maximum receive power P is signalled back to its serving BS.
The corresponding weights v i of the UTs best beam are now used for transmission if this certain UT is
scheduled. With single stream transmission (i.e. no SDMA), only one UT is scheduled to use one beam in
one chunk.
The antenna weights v i are calculated from the main beam direction ϑ i of beam i and from the m-th
transmit antenna element position d m for all M elements, with k = 2π/λ being the wave number, according
to:
1
v [ ( ) ( ) ( ) ] T
i
( ϑ
i
) = vi1
ϑi
vi
2
ϑi
... viM
ϑi
with vim ( ϑ) = exp( − jkd m
sin( ϑi
))
M
A four-element uniform linear array with λ/2 spacing has the element positions:
1 3
d
m
= ± λ , ± λ
4 4
For a 120° sector with four antenna elements, eight beams are chosen with beam directions according to
equal spacing in beamspace (resulting in equal beam crossing levels in the directivity pattern), which
gives the following beam directions ϑ i in degrees:
[-49.3000 -32.8000 -19 -6.2000 6.2000 19 32.8000 49.3000]
The resulting antenna pattern is shown in Figure 3-1 below.
R
Figure 3-1: Fixed beam pattern for a 120° sector with 70° HPBW, 8 beams created by 4 elements.
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3.1.3 Fixed beam design and scheduling for GoB SDMA
As mentioned above, SDMA can be implemented on top of beamforming as additional multiple access
component, in order to enhance the system performance via spatial re-use of the radio resources. Several
enhancements are possible for GoB when using SDMA. The shape of the beam directivity pattern can be
altered by tapering, which will be described in detail below. Adaptive scheduling gives additional
performance, e.g. the score based scheduler can be modified. One possible solution is described at the end
of this section.
Tapering can be used on top of the GoB to improve the shape of the beam directivity pattern for SDMA.
For the investigations Chebyshev tapering [Dol46] was chosen as it is optimal in the following sense: For
a given side lobe level, the width of the main lobe is minimised. When using SDMA with GoB,
increasing tapering decreases cross-talk between side lobes and increases robustness due to beam
mismatch. Figure 3-2 below illustrates a beam pattern with and without the side lobe suppression
resulting from tapering. For single stream GoB (non-SDMA) that was described in Section 3.1.2, tapering
is not recommended as lower side lobes are not needed there.
The calculation of the tapering vector gives real valued filter coefficients, denoted as t . The description
of the baseline GoB scheme in Section 3.1.2 above defines the design of the un-tapered weights
w
baseline
based on the steering vector and the beam directions which are equidistant in a cosine space. Tapering for
each beam k can now be done on top of it by element-wise multiplying the amplitudes of the complex
weights of baseline GoB by the tapering vector of the desired approach (with i denoting the index of the
antenna element):
w
tapered , i,
k
= wbaseline,
i,
k
Tapering results in unequal transmit power for each antenna element. A key question here is the
efficiency of the power amplifier. A possibility to rebalance the power between the transmit amplifiers is
to shift single or dual antenna traffic (e.g. like non-preamble broadcast channels) to the outer elements of
the array where the shared data channel for tapered SDMA GoB uses less transmit power than in the
centre elements. Diversity schemes would benefit from the antenna spacing and the power amplifiers
could be used efficiently.
⋅ t
i
a) With side lobe suppression b) Without side lobe suppression
Figure 3-2: Beam pattern for 8 transmit antenna elements creating 16 beams, with and without
side lobe suppression.
One important aspect of SDMA is how to select and schedule users. Here a method based on scheduling
score and best beam index is described. The score is calculated as follows: For the best beam a CQI
feedback per chunk is needed. For a given frame, for each chunk in frequency direction a CQI feedback is
available and the ranking of each user’s latest CQI feedback determines its score. Each chunk now is an
independent instance for user allocation. The user with the highest score is allocated first. When users and
thus corresponding beams are allocated, neighbour beams on each side are blocked in order to avoid
causing significant intra-cell interference. The number of blocked beams depends on the scenario, the
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SNR and number of beams used. Now the user with the second highest score will be allocated, except its
corresponding beam is already blocked. This approach is continued until all users are checked or a desired
maximum number of spatial streams (e.g. four) is reached.
3.2 Transmit diversity techniques
With multiple-antenna concepts at the transmitter, transmit diversity can be applied for increasing the
performance at the receiver side. The idea is to mitigate the deteriorating effects of fading; by transmitting
the signals from different antennas it is possible to improve the reception in the other end of the link due
to the increased diversity. This allows reduction of the total transmitted power and thereby the
interference spread in the system.
In a cellular system, transmit diversity techniques can be applied by utilising neighbouring base stations
as the multi antenna configuration, which is known as macro diversity. From an interference mitigation
point of view this is valuable since the user at the cell border between two cells typically experiences a
high level of interference. By utilising macro diversity techniques the situation for the cell border users
are improved due to the increased diversity, meaning that the total transmitted power and hence the
interference may be reduced.
Here we will focus on one traditional transmit diversity technique, so-called closed loop transmit
diversity, a macro diversity method called cellular cyclic delay diversity (C-CCD), and in addition macro
diversity approaches for MBMS services will be considered.
3.2.1 Closed loop transmit diversity
In an open loop transmit diversity scheme no feedback from the receiver to the transmitter is
implemented. Hence, the transmitter has no knowledge about the channel and therefore no information is
available for adjusting the transmit antenna weights. In opposition using a closed loop transmit diversity
scheme allows the BS to generate the weight vector adaptively with the help of the constant feedback
information from the UT. One well-known approach is described in [3GPP25214] and is summarised in
Figure 3-3 below for the case of two transmitting antennas:
BS
FBI i- 1
FBI
i
Slot i
Ant
1
Ant
2
S ( )
1
t
S ( )
2
t
Step 6
Weights Decision
in BS
Step 1
...... ......
Channel:
H = ( h 1
h2)
Step 5
Feedback Indicator, one bit per slot
UT
Step 2
Channel Estimation:
H ˆ = ( hˆ
ˆ
1
h 2
)
Step 3
Optimal Weight
Calculation:
φ
max
[ P(
w)
]
Step 4
Phase (back) Rotation
Quantization:
φ
Q
Figure 3-3: Illustration of closed loop transmit diversity.
The UT uses the signals transmitted both from antenna 1 and antenna 2 to calculate the phase adjustment
to be applied at BS side to maximise the UT receiver power. In each slot i , UT calculates the optimum
phase adjustment φ , for antenna 2, by using the current channel estimation result to achieve the maximal
received power. The optimum phase adjustment φ is then quantised into φ
Q
having four possible values
in π / 2 resolution. The weight w
2
is then calculated by averaging the received phases over two
consecutive slots. Algorithmically, w
2
is calculated as follow,
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w
1
=
2
j
cos( φ
i
) +
2
n
n
2 ∑ ∑
i=
n−1
i=
n−1
sin( φ ),
i
where,
For antenna 1, w
1
is constant,
⎧ π π ⎫
φ i
∈ ⎨0
π − ⎬ .
⎩ 2 2 ⎭
1 = 1/
2
w .
3.2.2 Cellular Cyclic Delay Diversity (C-CDD)
With multiple antennas at the transmitter, transmit diversity can be applied for increasing the performance
at the receiver side. These transmit diversity techniques can be based on, e.g., space time block codes
(STBCs) or antenna diversity schemes [DK01]. These multi antenna techniques can be shifted to a
cellular scenario by using the neighbouring base stations as the multi antenna setting. Therefore, transmit
diversity is transformed into macro diversity. In 2002, Inoue et al. [IFN02] proposed this within the
application of STBCs. In contrast to STBCs, the application of cyclic delay diversity (CDD) to a cellular
environment, namely cellular CDD (C-CDD) [PD06] offers the exploitation of the increased transmit
diversity at the receiver without any change on the receiver side. Transmitting the same signal from
several base stations including cyclic delays will be observed as a channel with higher frequency
selectivity at the receiver. This resulting additional frequency diversity can be collected by a channel code
for instance. There exists no rate loss for higher number of transmit antennas/base stations, and there are
no requirements regarding constant channel properties over several sub-carriers or symbols and transmit
antenna/base station numbers. The principle structure of C-CDD is presented in the block diagram of
Figure 3-4 without considering the random choice of cyclic delays and power adaptation blocks.
Figure 3-4: Principle of cellular cyclic delay diversity.
At the cell border of a conventional OFDMA system, inter-cell interference exists due to double allocated
sub-carriers, and therefore, the used sub-carrier resources are underachieved. This decreases the
exploitation of the sub-carrier resources per cell site. C-CDD takes advantage of the aforementioned
resources. The main goal is to increase performance by avoiding interference and increasing diversity at
the most critical environment directly at the cell border.
The described C-CDD technique offers an improved performance especially at the critical cell border
without the need of any information about the channel state information on the transmitter side. On the
other side, inter-BS communication is necessary to guarantee the transmission of the desired signals on
the same sub-carriers. Furthermore, the transmission from the BSs must ensure that the reception of both
signals is within the guard interval, and therefore, the involved BSs have to be almost synchronised.
Additionally, the inter-cell interference within a cell or sector is reduced by lowering the transmit power
for sub-carriers which are assigned to C-CDD. There is still a performance gain due to the existing macro
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diversity. The drawback of cancelled out cyclic delays due to the geographical setup can be avoided by
using a randomised choice of the cyclic delays. The area with higher diversity can be also broadened by
applying an adaptive transmit power control, cf. Figure 3-4. This adaptation will need a feedback of the
received signal power information.
3.2.3 Macro diversity for MBMS
The Multimedia Broadcast and Multicast Service (MBMS) is a unidirectional point to multipoint service;
data is sent from a single source to multiple recipients. MBMS can provide simultaneously downlink
services for multiple users in full area coverage without taking into account user location and radio
conditions. MBMS is seen as essential to support the full range of mobile TV and video services. Macro
diversity is considered as a possible enhancement to the MBMS. In a MBMS service the transmitted
content is expected to be network-specific rather than cell-specific. Therefore, a natural way of improving
the physical layer performance is to take advantage of macro diversity. Basically, the diversity combining
concept consists of receiving redundantly the same signal over two or more fading channels, and combine
these multiple replicas at the receiver in order to increase the overall received SNR. On the network side,
this means ensuring sufficient time synchronisation of identical MBMS transmissions in different cells;
on UT side, this means the capability to receive and decode the same content from multiple transmitters
simultaneously.
In the downlink, two types of networks can be distinguished:
• Multiple Frequency Network (MFN): several transmitters almost send simultaneously the
same signal over different frequency channels. Two receive schemes can be considered for
MFN:
o Hard combining scheme. The technique selects the best cell base station in terms of
o
path-loss and shadowing in order to further mitigate the adverse effects of interference.
Soft combining scheme. For multi-cell broadcast, soft combining of radio links can be
supported, assuming a sufficient degree of inter-BS time-synchronisation, at least
among a subset of BSs.
• Single Frequency Network (SFN): Several transmitters send simultaneously the same signal
over the same frequency channel. The user can employ a single receiver to demodulate the
superimposed signals but the SFN gain is sensitive to the temporal synchronisation of the signals
received from different BSs.
3.3 Receive diversity and interference suppression techniques
By equipping the radio receivers with multiple receive antennas, it is possible to implement different
combining schemes in the baseband signal processing. Since the radio channels (from a transmit antenna)
to the receive antennas tend to fade differently, multi antenna receivers provide receive diversity – both
for the signal of interest and for the interference. With appropriate selection of the antenna combining
weights, accounting for e.g. the radio channel, the interference power and the spatial colouring of the
interference, such multi antenna receivers may provide increased robustness to both fading and
interference. This, in turn, may improve the radio network coverage, capacity and user data rates.
Maximum ratio combining (MRC) and interference rejection combining (IRC) are two well-known
combining schemes. With MRC the combining weights are selected accounting for the radio channel (of
the desired signal), the noise power and the interference power at the different receive antennas. IRC is an
extension of MRC which also takes the spatial characteristics of the receive signals into account and
therefore enables interference suppression at the receiver. IRC determines the combining weights based
on the channel and the (spatial) noise and interference covariance matrix, i.e., not only the interference
power but also the spatial colouring of the interference is taken into account.
MRC and IRC can be applied both at the BS receiver and at the UT receiver, and is consequently possible
to use to improve both downlink and uplink performance.
3.3.1 Maximum Ratio Combining (MRC)
The MRC combines coherently the signals output by the various sensors, by applying weights depending
on the signal to noise ratio (SNR) at each antenna output. This means that the MRC can be seen as receive
beamforming, since the effective antenna pattern resulting from application of the antenna weights is a
beam towards the desired signal. As the true MRC requires the estimation of the noise power, the weights
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are generally approximated by being only proportional to the strength of the desired signal on each
antenna, provided by the corresponding channel estimate.
MRC provides diversity gain provided the fades experimented on each receive antenna are sufficiently
decorrelated. In addition, MRC provides coherent combining gain as the desired signals are added
coherently but not the interference. Note that when interference is spatially white, MRC provides optimal
performance according to the maximisation of the signal to interference and noise ratio (SINR).
Figure 3-5 illustrates the principle of spatial combining. In OFDMA, the combining has to be performed
on a sub-carrier basis, i.e. the combining weights depend on the sub-carrier. In the case of M R receive
antennas, the (approximated) MRC combining weights on a given sub-carrier are given by
T
w = [ w 1,
w2,
K , w M R
] = h
where h is the M R x 1 vector containing the channel coefficients of the signal of interest for the
considered sub-carrier.
Spatial combining
*
w
1
samples received
from antenna 1
samples received
from antenna 2
symbol
estimates
*
w
2
Figure 3-5: Principle of spatial combining in the case of two receive antennas.
3.3.2 Interference Rejection Combining (IRC)
IRC is an extension of traditional MRC that also accounts for the spatial structure of the interference,
which allows the interference to be partly rejected in the spatial domain [Vau88]. If we again make the
analogy to beamforming, this means that IRC can be seen as receive beamforming with null steering,
since the effective antenna pattern now also has nulls in the direction of the interferer(s). A receiver
equipped with M T receive antennas can perfectly reject M T -1 interfering sources, provided the interfering
signals are received with different spatial signatures, i.e. different directions of arrival (DoA), compared
to the signal of interest. When the number of sources are greater than M T -1, which is usually the case in
real-world systems, the IRC rejects as much interference as possible with a linear processing, in a way
that maximises the SINR at the receiver output.
The IRC weights are given by
w
IRC
⎪⎧
Ps
R
= ⎨
⎪⎩ α Γ
−1
−1
h
h
where P s is the transmit power of the signal of interest, R is the spatial correlation matrix of the received
signal, Γ is the correlation matrix of the interference + noise term, and α is a real positive factor which
can be omitted in the computation of the filter (as well as P s ). The direct computation of the IRC weights
consequently requires the estimation and inversion of either the correlation matrix of the received signal,
or the correlation matrix of the interference + noise term. As a consequence, the IRC receiver requires a
substantial increase of the receiver complexity compared to the MRC.
Note that when the interference is spatially white and is equal on each antenna, matrix Γ is proportional
to the identity matrix and the IRC weights then reduce to the weights of the MRC (up to a real positive
scaling factor that has no influence on the performance).
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The estimation of matrix R or matrix Γ can be performed according to two methods. The first one is
often called “parametric” since it relies on the knowledge of the underlying structure of the correlation
matrix. It involves estimating the channel and received power of each interferer, and then building the
correlation matrix (either one). This method requires means to estimate the interferers’ channel, the most
convenient being to be able to use the interferers’ pilots.
The second method estimates directly the autocorrelation matrix of the received signal, by means of
average sample products. In OFDMA the average can be done in time and/or frequency. A trade-off may
have to be found in the averaging time/frequency window, since the larger the window the more accurate
the estimation, but the less it accounts for the variations of the channel (in time or in frequency,
depending on the dimension over which is made the average). Nevertheless, this method does not require
any knowledge about the interferers’ pilots, since the correlations are computed from the received
samples (in frequency, after the FFT) directly. As a consequence, all the received samples of a chunk can
be used for this purpose.
Accurate channel and interference estimates are essential for IRC to work efficiently. As discussed above,
this can be achieved in different ways, but it is expected that this put requirements on the pilot design. In
addition, it is advantageous with a time synchronised network, but it is not a requirement; the IRC will
still work but the gain will not be maximised.
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4. Assessments
Assessment of the performance of the developed inter-cell interference mitigation methods is an
important task in order to verify that the theoretical ideas behind the methods are correct, and also to
identify in what situations the methods are particularly useful and so on. The assessment work can be
carried out in different ways, e.g. by theoretical analysis, but for the work on smart antenna based
interference mitigation it has mostly been done by computer simulations. Different simulations are
required depending on the required detail of the study, and also the particular aspect that is in focus of the
investigation. Since the focus of the assessments to large extent has been on system performance, mostly
system level simulations have been carried out, but some aspects have also been studied by link level and
multi-link simulations, e.g. complexity reduction techniques.
In the assessment of inter-cell interference mitigation techniques, there are some challenges that need to
be handled. One important challenge is how to model the inter-cell interference. The most straightforward
way is to fully model all interfering links, but this might be very complex and require large computer
resources for the simulations. Hence, it might be beneficial or even necessary to reduce the number of
interfering links, and this must in that case be done in an accurate manner. Another major challenge is to
ensure that the results from different simulators and partners are comparable. In particular for system
level simulators this is not a trivial task due to the large number of parameters and different models of e.g.
radio propagation effects, traffic, etc. that are involved, but also for link level and multi-link simulators
special emphasis needs to be put on calibration as far as possible. These challenges and how to tackle
them will among other things be discussed in Section 4.1 below, before more details on the actual studies
that have been carried out and their results will be presented in Section 4.2. For the interested reader,
further details are also available in the appendices.
4.1 Methodology, assumptions, and assessment criteria
The assessments of smart antenna based interference mitigation methods have mainly been conducted by
means of computer simulations. Most of the studies have been carried out as system level simulations, but
also link level and multi-link simulations have been performed. A classification of the simulators used
can be found in [WIN2D6131].
Link level investigations permit inter-cell interference to be studied from the one link’s perspective, and
the effectiveness of interference mitigation techniques to be assessed in terms of bit error rate (BER) or
block error rate (BLER) improvement at the receiver.
When analysing or assessing the overall performance of a radio network it is typically not sufficient to
study the performance of a single radio link. For instance, in a cellular network it must be considered that
the resources in a cell are shared among all user terminals associated with the cell and that the dynamic
behaviour of the user terminals will impact how those resources are used, and therefore the inter-cell
interference landscape. Multi-cell evaluations of cellular networks are often performed by computer
simulations, here referred to as system level simulations. In a system level simulation typically there is no
explicit real-time modelling of physical layer behaviour such as modulation and coding. Instead, a link to
system interface is used to provide an estimate of the performance of single links within the system.
Typically this will take as input a measure of the radio link quality (e.g. SINR) and provide as output an
estimate of packet error probability [BAS+05].
4.1.1 Inter-cell interference modelling
As indicated above, an important challenge for the assessment work is to accurately model the inter-cell
interference. When modelling inter-cell interference, all the properties of inter-cell interference exploited
by the mitigation techniques, or having an influence on the performance results, have to be reproduced in
the most realistic way. However, accurate simulation of all the radio links can be computationally
intensive, leading to unpractical simulation durations. Therefore, a trade-off has to be found between the
accuracy of the models and their implementation complexity.
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4.1.1.1 Link level inter-cell interference modelling
In order to study inter-cell interference at the link level, a number of system level characteristics need to
be accounted for, since the inter-cell interference is created by multiple nodes in the system, and their
behaviour. These factors can be modelled within a link level simulation by means of a system to link
interface, which provides snapshots of instantaneous system parameters, and their influence at the link
level. By analysing a suitable number and range of snapshots, a statistically meaningful analysis of
behaviour at the link level can be obtained. In order to further reduce simulation complexity,
simplification of modelled behaviours within a snapshot may be applicable. In particular, the least
significant interfering links may be ignored, and some of the remaining links may be modelled by
Gaussian approximation as shown in Appendix A.
Multi-link simulations consider an approach between that of link level and central-cell system level
simulations (see Section 4.1.1.2.1 below). As in the case of link level simulations, a number of
instantaneous snapshots of system parameters are studied. However, within each snapshot, the behaviour
of each separate interfering link, across a number of cells, is more accurately modelled. As in the other
methods, some links may be ignored, or approximated.
4.1.1.2 System level inter-cell interference modelling
When performing a system level simulation, one question to be addressed is how to model a number of
different cells. A real cellular network may consist of hundreds or thousands of cells, but it is not practical
to model such a network within a single simulation - it is only possible to model a limited number of
cells. Since the considered system layout is limited, the cells in the outer parts of the modelled network
are not surrounded by cells on all sides. The interference situation in these cells differs significantly from
the interference experienced in the central part of the network where cells are surrounded by interfering
cells on all sides. The performance of such a network is hence not representative of a large real-world
network in which basically all cells experience interference from all directions. Two popular techniques
exist to account for the impact of surrounding cells and inter-cell interference, namely the central cell
technique and the wrap-around technique.
4.1.1.2.1 Central cell technique
In the central cell technique for multi-link or system level simulations, results are only collected in the
central part of the simulated multi-cell layout. This is typically the central cell in an omni-directional
layout, or the three sectors of the central site in a tri-sectored layout, where the studied area is surrounded
on all sides by interfering cells. Only the system functions of the central site and the associated terminals
are simulated in detail, while the remaining cells are accounted for via simplified models.
Compared to a multi-cell simulation where UTs are generated in multiple cells, e.g. using the wraparound
technique (see Section 4.1.1.2.2 below), the central cell technique allows considerable savings in
memory resources, as only a reduced number of links needs to be monitored and managed at the same
time. From the simulation duration perspective, however, the central-cell technique may be equivalent to
a multi-cell simulation using the wrap-around technique, since more simulation time (i.e. more snapshots)
is needed to collect the same number of statistics on UTs within the central cell, compared to collecting
data from UTs studied simultaneously within a larger number of cells.
The central cell technique is particularly suited to the downlink, as it avoids the explicit simulation of the
UTs in the neighbouring cells. Indeed, the interference situation can here be generated accurately by
simulating the transmitted signal from the neighbouring base stations only. In the uplink, however, where
the interference is created by the signals transmitted from the UTs in neighbouring cells, an accurate
modelling of their positions, transmit powers and channels towards the BS of interest cannot be avoided
in most cases. Hence the savings in simulator complexity may be much lower in the uplink case.
4.1.1.2.2 Wrap-around technique
The use of wrap-around, described e.g. in [ZK01], is one way to overcome the limitation of a limited
system. With wrap-around the cell layout is folded such that cells on the right side of the network are
connected with cells on the left side and, similarly, cells in the upper part of the network get connected to
cells in the lower part. The created area may be seen as borderless, but with a finite surface, and it may be
visualised as a torus.
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Figure 4-1 below shows an example of the wrap-around technique in which a hexagonal cell layout
comprising 19 base stations with 120 degrees cell sectors is considered. In this example, each base station
is located at the centre of one hexagon with one transceiver per sector. In an inter-cell interference
scenario each transceiver is considered as an interferer to other victims. There is a one-to-one mapping
between cells/sectors of the centre network and cells/sectors of each copy, so that every cell in the
extended network is identified with one of the cells in the central (original) hexagonal network. Those
corresponding cells have thus the same antenna configuration, traffic, fading etc., as illustrated in Figure
4-1 below. Among the seven calculated channels, the one with the lowest attenuation is selected for
further use. Typically, the selection is performed based on long-term channel information including
distance attenuation, shadow fading, and antenna gains. Short-term channel variations caused by multipath
propagation and fast fading may later be added to get a full representation of the frequency-selective
channel.
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Figure 4-1: Illustration of the wrap-around technique.
The wrap-around technique is suitable both for downlink and uplink simulations. An advantage compared
to the central cell technique is that simulation data can be collected from all cells, which may reduce the
required simulation time to collect sufficient statistics.
4.1.2 Assumptions
Another challenge for the assessment work is to ensure that the results from different partners and
simulation environments are comparable. Ideally, common simulators or tightly calibrated simulators
would be used. However, the resources required to achieve this across multiple partners are prohibitive.
Instead a number of common scenarios, simulation parameter sets and assumptions have been defined.
In general, simulation parameters and assumptions considered in the different studies are based on the
baseline design defined in [WIN2D6137], where three test scenarios are defined. These are base coverage
urban which considers a wide area (WA) deployment, microcellular which is a metropolitan area (MA)
scenario, and finally an indoor or local area (LA) scenario. The first one is based on the FDD PHY mode
with 2x50 MHz bandwidth, while the latter two are based on the TDD PHY mode and uses 100 MHz
bandwidth. Depending on the objectives of the investigations, minor deviations from baseline parameters
have been made in some studies, which are then clearly stated. The reasons for these differences can be
several, e.g. due to restrictions in the simulation environments, but also due to that particular aspects are
of interest in the study. One such example could be the impact of the scheduling strategy.
The investigations in this deliverable are carried out within the base coverage urban WA scenario.
Techniques addressing the receiver only (i.e. MRC, IRC) are equally applicable to all scenarios, and the
general conclusions can be applied widely, although exact numerical results may vary. Transmitter
techniques may not always be supported in all scenarios (e.g. reduced functionality radio nodes, such as
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relays or home base stations may not support GoB), but when the technique can be supported, similar
conclusions can also be expected.
Both uplink and downlink are studied, with an emphasis on downlink. Similarly, most studies use a full
buffer traffic model, although more realistic traffic models are used in some cases. Detailed descriptions
of various traffic models can be found in Appendix A in [WIN2D6137].
For link level, and multi-link, simulations a number of different victim UT locations are considered. The
base coverage urban scenario deployment is a three-sector hexagonal cellular layout with inter-site
distance of 1 km, as defined in [WIN2D6137]. The studied UT positions are given in Table 4-1, and
illustrated in Figure 4-2.
Table 4-1: UT positions for link level simulations in Base Coverage Urban scenario.
UT location Distance from BS LOS angle from sector antenna array
broadside
Cell-centre 50m 0
Cell-edge 660m 0
Sector-border 160m pi/3 radians
Figure 4-2: UT positions (red dots) for link level simulations in Base Coverage Urban scenario.
For system level simulations, both central cell and wrap-around techniques are used, as described in
Section 4.1.1.2.
4.1.3 Assessment criteria
There are a wide range of different assessment criteria which can be applied to inter-cell interference
mitigation studies, covering the impact of a scheme directly on performance of a single function within a
node, through to the overall end-to-end (E2E) performance of a system within which nodes are equipped
with particular inter-cell interference mitigation schemes.
A list of assessment criteria is provided in [WIN2D6137]. From these, the most important criteria for
inter-cell interference mitigation studies are:
• CDF of the user throughput
• CDF of the (chunk) SINR
• The average sector throughput
• BER
CDF of user throughput seems to be the most important one, because it can be used for deriving the
standard performance measure criteria like fairness, average throughput and spectrum efficiency. The
gain of smart antenna based interference mitigation techniques can sometimes be read directly from the
CDF of SINR: e.g., gain in mean SINR, decreasing the percentage of users having SINR below certain
threshold etc. Chunk SINR is defined as the SINR after receiver processing but prior to decoding and
arithmetically averaged over the symbols of a chunk. Since networks’ providers are often interested in
maximising average performance per cell or sector, the average cell/sector throughput is also an important
performance criterion. When studying inter-cell interference mitigation from a link level perspective,
BER is a performance criterion to consider.
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Delay distribution is also an important performance criterion for E2E evaluations, since delay
experienced by a user has a great impact on user’s satisfaction, especially in the case of real-time services.
However, E2E delay is impacted by many different parts of the system functionality, and cannot be
assessed solely within inter-cell interference mitigation studies. Improved CDF of SINR coming from
inter-cell interference mitigation provides an input into overall E2E performance (e.g. impact on
scheduling and resource management) which can be used towards estimating the E2E delay.
Performance of the studied schemes based on the assessment criteria will also be sensitive to the overall
design and assumptions related to environment and deployment. For instance, the performance of an
interference mitigation scheme may depend on the resource management/scheduling strategy used.
Accordingly, when analysing the performance of a specific strategy, the other assumptions should be well
described and the impact of changing these other strategies/assumptions should be investigated as far as
possible.
In addition to objective technical performance metrics, other assessment criteria, such as complexity,
overheads and impact on system architecture/design, are considered.
4.2 Results
The studies that have been performed can be divided into three main categories. The first one is downlink
interference mitigation with multiple antennas, where the studies are focused on the use of transmit
beamforming techniques at the BSs, but also on receive diversity and interference suppression techniques
based on multiple antenna reception in the UTs. The second category is uplink interference mitigation
based on multiple antennas, where the use of receive diversity and interference suppression at the BS is
assessed. Finally, the third category is macro diversity, where different macro diversity techniques, i.e.
transmit diversity from several BSs, are studied.
4.2.1 Downlink interference mitigation with multiple antennas
In this section we will present results on downlink interference mitigation with multiple antennas. The
techniques that have been used are transmit beamforming at the BS, and receive diversity and interference
suppression techniques in the UTs. First we will present results on different combinations of these
techniques for the non-frequency adaptive mode, then we will show how the frequency-adaptive mode
affects these results and also how SDMA on top of the transmit beamforming affects the inter-cell
interference mitigation properties. We will also show results on how beamforming interplay with
different scheduling and channel allocation strategies. In addition, implementation aspects of interference
suppression schemes in a UT receiver will be covered.
4.2.1.1 Non-frequency adaptive transmissions
This study investigates the use of multiple antennas for downlink inter-cell interference mitigation in the
spatial domain. At the BS, the multiple antennas are used for transmit beamforming. In this study the
GoB scheme, described in Section 3.1.2 above, will be used. The multiple receive antennas at the UT are
used to implement combining schemes in the baseband signal processing, in this case we will investigate
traditional MRC and IRC, which are described in Section 3.3 above.
The evaluations are done by means of system level simulations of a non-frequency adaptive
OFDMA/TDMA network with 19 sites, each with three sectors (cells). All links are modelled in the
simulations, and the wrap-around technique is used to account for border effects. Round robin TDMA
scheduling is used, i.e., in each frame a single user per sector is assigned to the entire transmission
bandwidth of 40 MHz. Apart from that, most assumptions follow the base coverage urban scenario
defined in [WIN2D6137].
The results are summarised in Figure 4-3 below which shows the average sector throughput for different
techniques and combinations of techniques. SISO is a reference case with one sector-covering transmit
antenna at the BS and single receive antennas at the UTs, while GOB refers to GoB transmission from the
BS. MRC(x) and IRC(x) refers to MRC and IRC reception at the UTs utilising x receive antennas. It can
be seen that both transmit beamforming and terminal multiple antenna reception are efficient means to
mitigate inter-cell interference. With SISO the average sector throughput reaches almost 40 Mbps, which
corresponds to around 1 bps/Hz/sector. Introducing terminals with dual receive antennas using MRC
increases the average throughput by 38 %, while IRC gives additional 22 %. Increasing the number of
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receive antennas to four enhances the performance even further, indicating that this might be an
interesting alternative for devices that can accommodate the antennas, e.g. laptop computers. The real
performance boost comes however from the GoB transmission; by going from SISO to GoB transmission
the throughput is doubled, and by combining it with dual antenna MRC and IRC the gain over SISO is
155 % and 184 %, respectively. This means that with GoB transmission and terminal dual antenna
reception with IRC it is possible to reach almost 3 bps/Hz/sector. Hence, the two techniques (transmit
beamforming and terminal multiple antenna reception) complement each other very well. The reasons for
this are two; first, the GoB transmission reduces the inter-cell interference spread in the network
significantly since the probability that neighbouring base stations direct their transmissions in the same
direction is significantly reduced. Secondly, when this still happens the interference is most probably
strongly dominated by a single source, which is exactly the situation that most interference suppression
techniques for dual receive antennas are designed for. In other words, the transmit beamforming creates
an interference environment that is particularly favourable for e.g. IRC. It should, however, be noted that
these results will change if SDMA is implemented on top of the GoB scheme, since that means that the
same time-frequency resources will be transmitted in different beams at the same time. This implies that
the strong directivity property of the interference will be reduced, meaning that the probability for beam
collisions increases as well as the probability for more interfering sources which reduces the efficiency of
e.g. IRC. This is further investigated in Section 4.2.1.3 below.
160,0
average sector throughput [Mbps/sector]
140,0
120,0
100,0
80,0
60,0
40,0
20,0
0,0
SISO
MRC(2)
IRC(2)
MRC(4)
IRC(4)
GOB
GOB+MRC(2)
GOB+IRC(2)
GOB+MRC(4)
GOB+IRC(4)
Figure 4-3: Average sector throughput.
The results above are focused on system performance. In Appendix B, further results can be found, e.g.
on performance for users at the cell border. It is shown that for this purpose the techniques provide even
larger gains. For example, GoB transmission and dual antenna reception with IRC improves the
performance by more than 500 % for users at the cell border.
4.2.1.2 Impact of frequency adaptivity
In this study also frequency-adaptive transmissions are considered. The multiple antenna based
interference mitigation techniques are still GoB with four transmit antennas at the BS, as well as receive
diversity with MRC and IRC at the UT.
The evaluations are carried out using a class III system level simulator [WIN2D6131], under the base
coverage urban scenario. Only the downlink is considered, with a full buffer traffic model. The total
number of users in each sector is 24 in average. The UT always uses two receive antennas. The multi-cell
environment is accounted for using the central cell technique. All the interfering BSs are assumed to
transmit at full power.
Table 4-2 and Table 4-3 below summarise the results obtained for the frequency non-adaptive and
frequency-adaptive mode, respectively. Further results can be found in Appendix C.
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Table 4-2: Summary of results for frequency non-adaptive mode:
Scheme
1 Tx
MRC
1 Tx
IRC
GoB 4Tx
MRC
GoB 4Tx
IRC
Sector service throughput
(Mb/s)
47
+0%
54
+16%
74
+58%
84
+80%
Cell-edge service
throughput (kb/s)
120
+0%
285
+140%
990
+720%
1100
+810%
Table 4-3: Summary of results for frequency-adaptive mode:
Scheme
1 Tx
MRC
1 Tx
IRC
GoB 4Tx
MRC
GoB 4Tx
IRC
Sector service throughput
(Mb/s)
86
+0%
94
+9%
95
+10%
105
+21%
Cell-edge service
throughput (kb/s)
320
+0%
800
+150%
1500
+370%
1800
+460%
IRC at the receiver and GoB at the transmitter appear as very efficient means to combat inter-cell
interference. Furthermore, both techniques can be combined in order to provide the best performance, and
complement each other well. As was also indicated in Section 4.2.1.1 above, the GoB significantly
reduces the interference, and when interference still occurs it is often a strongly dominating source in the
form of a beam from a neighbouring BS.
The gains brought by the multiple antenna techniques are generally lower in the frequency-adaptive mode
than in the non-adaptive mode, but still remain very attractive. This behaviour is partly explained by the
robustness w.r.t. inter-cell interference provided by the frequency adaptivity, even in the absence of
interference mitigation processing. More particularly, the relative gains of the GoB in terms of cell edge
throughput are approximately reduced by a half in the frequency-adaptive mode compared to the nonadaptive
mode, whereas the IRC gains (with single-antenna transmission) remain approximately the same
for both modes. The reason is that the interfering beams, and thus the interference environment the cell
edge users are particularly sensitive to, change at each frame in an unpredictable manner for the UT. The
benefits of frequency adaptivity are consequently reduced, since this mode relies on the channel quality
predictability. For the same reason, the cell edge throughput improvement brought by IRC over MRC
with GoB transmission is higher in the frequency-adaptive mode (+20 % vs +11 % in the non-adaptive
mode), although is roughly the same in both modes with single-antenna transmission. By allowing the
interfering beams to be efficiently mitigated when they hit the UT, IRC has the ability to smooth the
interference variability induced by the GoB, and thus to improve the reliability of the link adaptation.
4.2.1.3 Impact of SDMA
Taking into account the inter-cell interference situation beamforming techniques using fixed GoB are
investigated with the aim to show the expected improvement achieved when used in combination with
SDMA. The spacing between the transmit antennas is half a wavelength; and two resp. four transmit
antennas are used to build four resp. eight beams per sector. Receive diversity at UT side is achieved by
the MRC scheme. When used with SDMA the fixed GoB scheme built by four antennas is enhanced by
the tapering algorithm introduced in [WIN2D341] and described in Section 3.1.3 above. With this
scheme, the shape of the beam directivity patterns is altered in a way that better spatial separation is
achieved for the scheduled users. Adaptive scheduling is used with further enhancements as indicated in
Section 3.1.3. The user selection is based on scheduling score and best beam index, which requires CQI
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feedback to determine the score. Up to three users with spatially independent data streams are served
simultaneously.
For comparison of the schemes, the closed loop transmit diversity method described in Section 3.2.1 is
taken into account rather than a technique without any diversity at BS side. The base coverage urban
scenario with channel model C2 [WIN2D111] is considered for the investigations which are conducted by
means of system level simulations (Monte Carlo snap-shots based simulator class III) for the frequencyadaptive
case. The inner three-sector site is surrounded by a first tier of six neighbouring sites and the
wrap-around technique is used. The number of users per sector is 10 in average, all moving with the same
velocity of 3 km/h. The scheduling algorithm used is the score based proportional fair method as specified
in [WIN2D6137]. All links are simulated and all cells taken into account for the evaluations. More details
on the simulation assumptions are given in Appendix D.
The performance achieved by the different techniques for the average sector throughput is shown in
Figure 4-4 below. The best performance is reached by the SDMA technique combined with the tapered
fixed GoB built by four antennas which brings here a significant gain of approximately 47 % when
compared to the closed loop transmit diversity method.
average sector throughput
140.00
120.00
av sector TP (Mb/s)
100.00
80.00
60.00
40.00
20.00
0.00
Tx: diversity
closed loop 2
Rx: 2 (MRC)
Tx: fix GoB 2
Rx: 2 (MRC)
Tx: fix GoB 4
Rx: 2 (MRC)
Tx: tap.fix GoB 4
Rx: 2 (MRC)
& SDMA
Figure 4-4: Average sector throughput.
The 5 th percentile of the CDF of the average user throughput is used to measure the performance at the
cell border. Its behaviour and the corresponding detailed values are shown in Appendix D. This
assessment criterion is increased when using a GoB instead of closed loop transmit diversity at BS side by
18 % with the same number of antennas (two). However, GoB built with four antennas performs better
(41 % gain) than the tapered fixed GoB with the same number of antennas used together with SDMA (27
% gain). This difference of performance at cell edge of 14 % between both techniques can be explained
by the fact that the latter has been optimised so far considering mostly the interference situation inside the
cell rather than specifically at the cell border. The tapering of adjacent beams brings better isolation
between the scheduled users. Furthermore, interference is better spread by simultaneous transmission in
several beams keeping the same overall transmit power at BS side. But this degrades the situation at cell
border, due to the partly lost directivity of the interference compared to single stream beamforming.
Possible improvements especially on the scheduling scheme used should be further investigated.
Nevertheless this scheme brings the significant performance gain on the average sector throughput of
almost 24 % compared to GoB with four antennas; the corresponding value of the spectral efficiency is
increased from approximately 2.0 bps/Hz/sector to 2.5 bps/Hz/sector.
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4.2.1.4 Interplay between beamforming, scheduling, and channel allocation strategies
The impact of transmit beamforming on interference in downlink in presence of some interference
averaging techniques like Random Dynamic Channel Allocation (DCA) [WIN2D471] or interference
avoidance techniques like Minimum Interference DCA [WIN2D472] has been investigated. Furthermore,
also interplay between beamforming and scheduling strategies and their relative impact on interference
and system performance was studied.
The investigations were made by class II fully dynamic system level simulator [WIN2D6131] in base
coverage urban scenario and downlink according to WINNER baseline [WIN2D6137], and an adaptive
beamforming technique was considered, cf. Section 3.1.1 and Appendix E.
The results in Figure 4-5 show that the highest interference reduction and throughput gain comes from
Proportional Fair scheduling and then from beamforming (in Figure 4-5 denoted SA, i.e. smart antennas),
whereas the gain from Minimum Interference DCA is relatively low. The gains of DCA, scheduling and
beamforming are not additive regarding the interference or user throughput. The beamforming gain in
interference reduction is almost load independent, but it improves the throughput significantly mainly for
lower loads. The main gain in throughput comes from scheduling, which effectively exploits channel
variations at users’ receivers.
Mean Interference [dB]
-93
-94
-95
-96
-97
-98
-99
-100
-101
-102
0 10 20 30 40 50 60
Number of users per cell
Min I / Prop Fair
Min I / Round Robin
Rand DCA / Prop Fair
Rand DCA / Round Robin
Rand DCA / Prop Fair / SA Min I / Prop Fair / SA
Rand DCA / Round Robin / SA
(a) Mean interference
E2EThroughput [kbps]
2000
1800
1600
1400
1200
1000
800
600
400
200
0
0 10 20 30 40 50 60
Number of users per cell
Min I / Prop Fair
Min I / Round Robin
Rand DCA / Prop Fair
Rand DCA / Round Robin
Rand DCA / Prop Fair / SA Min I / Prop Fair / SA
Rand DCA / Round Robin / SA
(b) User throughput
Figure 4-5: Results for different combinations of interference mitigation methods.
4.2.1.5 Complexity reduction of IRC weight calculation at UT receiver
As has been seen above, multiple antennas can be used at the UT for mitigating the interference
experienced. This can be achieved by an appropriate choice of the combining weights used to sum the
signals arriving at the UT’s antennas.
This study addresses computational complexity for the MMSE approach for choosing Interference
Rejection Combining (IRC) coefficients. The calculation of IRC coefficients involves multiple matrix
inversions, which create a significant computational load. Options to reduce the computational
complexity, benefiting UT battery life, whilst minimising any degradation to performance, are studied
here. Through the use of common pilots, it is assumed that knowledge of the channel of pilot-carrying
sub-carriers is available. Interpolation techniques are used to enable calculation of IRC coefficients for all
other sub-carriers. It is in these interpolation techniques that we aim to reduce complexity.
Two different techniques are studied:
• In the channel interpolation (CHAN INP) case, the channel coefficients for the data-carrying
sub-carriers are obtained from the channel coefficients of the pilot-carrying sub-carriers by use
of linear interpolation. Then the IRC coefficients are calculated for each sub-carrier, by matrix
inversion. More details are given in Appendix F.
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• In the equaliser interpolation (EQUZ INP) case, first the IRC coefficients for the pilot-carrying
sub-carriers are calculated, then the IRC coefficients for the data-carrying sub-carriers are
obtained by linear interpolation of the IRC coefficients for the pilot-carrying sub-carriers. Matrix
inversions are only required for the pilot-carrying sub-carriers. More details are given in
Appendix F.
The evaluations are performed as computer simulations of a chunk based OFDMA/TDMA system, using
the base coverage urban scenario [WIN2D6137]. The studies consider a single UT receiving wanted and
interference signals from at least 19 BS sites, each with three sectors (cells) per site.
Figure 4-6 shows the SINR performance for three cases by using different receiver techniques where the
curve with label “ideal” means the channel is fully known and fully modelled. For “CHAN INP” and
“EQUZ INP” cases, only the important interference links whose cumulative power is eighty percent of
the total interference power are considered where the interference power here is calculated based on large
scale fading.
1
0.9
0.8
0.7
IDEAL
CHAN INP
EQUZ INP
Cell Edge
SINR performance for different receiver techniques
0.6
CDF (%)
0.5
Sector Border
Cell Centre
0.4
0.3
0.2
0.1
0
-20 -10 0 10 20 30 40
SINR per chunk (dB)
Figure 4-6: SINR performance for different receiver techniques.
From Figure 4-6, it can be seen that the SINR performance of “CHAN INP” and “EQUZ INP” match
each other tightly. However, in the computational complexity point of view, for the “EQUZ INP” method,
firstly, the computational effort used by the interpolation process to obtain the channel coefficients for the
data-carrying sub-carriers can be saved. Secondly, the matrix inversion for the data-carrying sub-carriers
when calculating combining coefficients can also be avoided. Since the computational complexity of the
interpolation operation is identical for these two methods, at least (N-N P )th matrix inversion complexity is
reduced by using “EQUZ INP”, hence this is the recommended technique. The performance degradation
between these two methods and the ideal cases is caused by using channel interpolation or combining
coefficient interpolation for the data-carrying sub-carriers and neglecting unimportant interference links.
4.2.2 Uplink interference mitigation with multiple antennas
In this study, the use of multiple antennas at the BS for uplink inter-cell interference mitigation in the
spatial domain will be investigated and assessed. The multiple receive antennas at the BS are used to
implement MRC and IRC in the baseband receive processing, as described in Section 3.3 above.
The evaluations are done by means of system level simulations of a non-frequency adaptive
OFDMA/TDMA network with 19 sites, each with three sectors (cells). Each sector is equipped with an
antenna array comprising two, four or eight elements separated half a wavelength, and MRC or IRC is
implemented for the antenna combining. The user terminals employ single antenna transmission in all
cases. Two types of scheduling are used: The first one is round robin TDMA, i.e., in each frame a single
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user per sector is assigned to the entire transmission bandwidth of 40 MHz. Due to the limited available
transmission power of the UTs, this is expected to be suboptimal. Therefore also a round robin
TDMA/FDMA strategy is simulated where eight users are frequency multiplexed, each using
approximately 5 MHz bandwidth. Apart from that, most assumptions follow the base coverage urban
scenario defined in [WIN2D6137].
The results of the study are summarised in Figure 4-7 below which shows the average sector throughput.
SISO is the reference case with one receive antenna at the BS, while MRC(x) and IRC(x) refers to MRC
and IRC reception utilising x receive antennas. As expected, it can be seen that the TDMA/FDMA
scheduling approach in all cases is better than TDMA scheduling since the frequency multiplexing
resulting in higher power per sub-carrier (due to the limited transmission power of the UTs) is beneficial
and sums up to higher system throughput. Hence, in the following we will focus on the results for
TDMA/FDMA scheduling, while further details on the TDMA scheduling results can be found in
Appendix G. With SISO and TDMA/FDMA scheduling the average sector throughput is slightly above
30 Mbps. By going to two receive antennas and MRC this is increased to around 45 Mbps, i.e. an increase
of almost 50 %, and by going to four antennas with MRC the throughput reaches about 62 Mbps which is
an increase of 100 % compared to SISO. With eight antennas and MRC the throughput reaches 86 Mbps,
which is almost three times the SISO throughput. The relative gain of IRC compared to MRC is rather
limited. With two antennas the throughput gain of IRC is only in the order of 2 Mbps (5 %) while for four
antennas it is around 6 Mbps (10 %) and for eight antennas 10 Mbps (11 %). The reason for this limited
additional gain of IRC can be discussed, but two theories are most probable. The first one is the antenna
separation of half a wavelength. With larger antenna separation, e.g. in the order of ten wavelengths, the
IRC gain should probably be larger. However, results in Appendix G indicate that this is not the case.
Hence, the reason is probably the colour of the interference. Results in Appendix G shows that the system
indeed is interference limited, which might give the impression that IRC should be very efficient.
However, if the interference consists of several interferers of similar strength, it means that the colour
goes towards white, meaning that the performance of IRC goes towards that of MRC, cf. Section 3.3.2. In
uplink we can expect more interferers than in the downlink, which consequently would result in lower
IRC gain.
120,0
average sector throughput [Mbps/cell]
100,0
80,0
60,0
40,0
20,0
TDMA
TDMA/FDMA-8
0,0
SISO MRC(2) IRC(2) MRC(4) IRC(4) MRC(8) IRC(8)
Figure 4-7: Average sector throughput.
Additional results in Appendix G show that also the coverage is significantly improved by having
multiple antennas at the BS. For example, by going from a single receive antenna to two antennas and
MRC gives an increase of 200 %, and by going to four and eight antennas this is even further increased. It
is also shown that having an antenna separation of ten wavelengths instead of half a wavelength
significantly increases the performance. However, this antenna setup is not in line with the GoB setup that
is proposed for downlink transmissions.
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4.2.3 Macro diversity
In this section we will present results on macro diversity. First an investigation of the C-CDD based
macro diversity technique introduced in Section 3.2.2 will be presented, and then we will show how
different receiver strategies perform in relation to SFN and MFN macro diversity for MBMS, as was
described in Section 3.2.3.
4.2.3.1 Link level investigations of C-CDD based macro diversity
In the following investigations on the performance and the simulation setup by applying the C-CDD
technique described in Section 3.2.2 are given. The C-CDD technique can be applied in any cellular
scenario in which the cells or sectors are overlapping, and therefore, C-CDD is independent of the
scenarios. Since the BSs have to be coordinated to transmit simultaneously the desired signals including
the cyclic delays, an inter-BS communication is required. Furthermore, the same sub-carrier resources at
each BS have to be available for C-CDD. There are no requirements at the UT, e.g., signalling of
information, to exploit the increased transmit diversity.
The evaluations are performed as computer simulations of non-frequency adaptive OFDMA/TDMA on
the link level for the base coverage urban scenario defined in [WIN2D6137] with the channel model
WINNER C2 for BS to outdoor UT [WIN2D111]. Exemplarily, the studied deployment comprises two
cells. The basic setup of the baseline system configurations are assumed in the simulations and a
cyc
convolutional code is chosen [WIN2D6137]. For the cyclic delay we assume δ
1
is set to 30 samples.
Figure 4-8 represents the bit error rate (BER) versus the carrier to interference (C/I) ratio. For C-CDD the
term C/I is misleading, as the transmitted signal from the interfering BS is no I (interference). On the
other hand it describes the ratio of the power from the desired BS to the temporary BS and indicates
where the UT is in respect to the BSs. For negative C/I values in dB the UT is closer to the interfering BS
and for positive C/I values is the UT nearby the desired BS. The cell border is defined for C/I = 0 dB.
Figure 4-8: BER versus C/I for SNR = 5 dB using C-CDD with full power and halved power per
sub-carrier, and no transmit diversity technique.
The performance of the applied C-CDD method is compared with the OFDMA reference system using no
transmit diversity technique and with a random independently chosen sub-carrier allocation in each cell.
The reference system is half (RL=0.5) and fully loaded (RL=1.0). We observe a large performance gain
in the close-by area of the cell border (C/I=-10dB…10dB) for the new proposed diversity technique C-
CDD. Furthermore, C-CDD enables an additional substantial performance gain compared to pure macro
cyc
cyc
diversity by transmitting the identical signals from both cells ( δ 1
= 0 ) at the cell border. For δ 1
= 0
no transmit diversity is available at the cell border. The same effect can be seen for C-CDD at C/I = -4.6
cyc
dB because the artificial delay δ
1
= 30 and the inherent geographical delay cancel out each other. Since
both BSs in C-CDD transmit the signal with the same power as the single BS in the reference system, the
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received signal power at the UT is doubled. For higher C/I ratios, i.e., in the inner cell, the C-CDD
transmit technique lacks the diversity from the other BS, and therefore, the performance merges to the
reference performances. To establish a more detailed understanding we analyse the C-CDD with halved
transmit power. For this scenario, the total designated received power at the UT is equal to the
conventional OFDMA system. There is still a performance gain due to the exploited transmit diversity for
C/I < 5 dB. The performance characteristics are the same for halved and full transmit power. The pure
cyc
macro diversity scenario ( δ 1
= 0 ) at C/I = 0 dB also represents the conventional OFDMA single-user
case without any inter-cell interference. The cell sites benefit from the halved transmit power for the used
C-CDD sub-carriers because a reduction of the inter-cell interference is achieved.
Further results on the possibility to broaden the diversity area, and therefore, to enlarge the performance
gain of the C-CDD technique by applying an adaptive approach, are given in Appendix H.
4.2.3.2 Evaluations of macro diversity techniques for MBMS
This study presents a performance comparison of SFN and MFN macro diversity applied for MBMS, as
was described in Section 3.2.3. In terms of performance, the MFN with soft combining overcomes all the
other schemes, as seen in Figure 4-9. The MFN with soft combining SNR target is about 1.5 dB better
than the SFN SNR target, but the MFN cost in terms of occupied sub-carriers is twice. So there is a tradeoff
between performance and resource consumption, and the choice of network will depend on the load of
the cell (and in particular on the number of chunks allocated to dedicated services).
Figure 4-9: Performance comparison of SFN and MFN – Perfect channel estimation.
Further results of the study, shown in Appendix I, also presents the impact of time synchronisation and
frequency offset errors on the performance. It is shown that the channel estimation quality is very
sensitive to these impairments, which affects significantly the performance. The study was carried out
using the WINNER baseline pilot design, where only one pilot is present per B-EFDMA block (see
Appendix I). Increasing the number of pilots per B-EFDMA block in the frequency dimension should
improve the resistance of the channel estimation.
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5. Recommendations for interference mitigation
This chapter provides recommendations for the WINNER system about the use of multiple antenna
techniques to mitigate inter-cell interference, based on the assessments performed in Chapter 4 together
with the requirements and constraints of the investigated methods presented in Chapter 3.
At the BS, transmit beamforming in the form of Grid of Beams (GoB) has proven high efficiency w.r.t.
inter-cell interference reduction, at the price of low signalling requirement if each user reports only one
average “best beam” for the whole bandwidth. Gains of more than 50 % in average sector throughput and
more than 300 % in terms of cell edge throughput (when defined at the 5 th percentile of the CDF) have
been reported for the non-frequency adaptive mode. The gains provided by GoB have two origins: the
first one is the use of GoB at the transmitter, which enhances the useful signal power by focusing the
energy in the direction of the UT. The second results from the use of GoB at the interfering BSs, which
lowers significantly the experienced interference level because the directivity of the interference
decreases the probability for a UT to have its assigned resources used at the same time in a colliding
beam. If the first gain contribution is always ensured by using GoB at the serving cell, the second depends
greatly on the spatial processing used at the neighbouring BSs. For instance, SDMA with tapered beams
on top of GoB has shown to be able to increase the sector throughput by 24 % with four antennas,
however at the expense of a lower cell edge throughput (-8 %) due to the spread of the interference
created to the other cells over several beams, even if each beam is also transmitted with a reduced power.
Since real-world networks are likely to use a combination of GoB and SDMA in the inner part of the
cells, and GoB without SDMA for cell edge users, the gains brought by GoB are expected to be lower in
real-world systems than reported in this report, especially for the cell edge users. Lower bounds on the
GoB gain against inter-cell interference can be quantified by assessing the GoB in the presence of fully
non-directive inter-cell interference, which however requires further studies.
Transmit beamforming at the BS, e.g. in the form of GoB, has thus been confirmed as a very powerful
technique to reduce inter-cell interference. However, its directivity prevents it from being used for
common control channels (e.g. the BCH), which have to be broadcasted over the whole cell. GoB
therefore represents a solution only for data channels.
Macro diversity, which transforms part of the interference in useful signal, has also been studied as a
means to mitigate inter-cell interference, mainly at cell border areas. Macro diversity can be used for
point to point communications, however at the expense of a double consumption of radio resources, as
well as an increased traffic load on the backhaul network in order to make the user data available at each
BS. These heavy drawbacks are suppressed in the case of MBMS, where the same information signal is
broadcasted to several receivers. Two main transmission schemes have been studied for macro diversity:
the Multiple Frequency Network (MFN) scheme and the Single Frequency Network (SFN) scheme, as
well as several receiver strategies. In addition, an enhancement of the SFN strategy, called Cellular Cyclic
Delay Diversity (C-CDD), has been proposed to further improve the macro diversity gain by introducing
frequency diversity. The MFN with soft combining has been shown to outperform the SFN without C-
CDD by 1.5 dB, but at the expense of a double frequency resources occupation. Further studies would be
necessary to compare MFN with C-CDD. From the implementation complexity point of view, the same
band should be used for MFN transmissions in order to avoid the need for demodulating several bands in
parallel for the UT. Still, MFN requires one distinct FFT per transmitting BS in order to account for
possible time-desynchronisation of the received signals due to the different propagation times. With this
respect, the requirements imposed by MFN on the time synchronisation are less stringent than the SFN,
which requires accurate network time synchronisation.
Beamforming techniques, e.g. GoB, can be applied without restriction in combination with macro
diversity techniques. However, beamforming is not applicable for MBMS, since the advantage of MBMS
is to use common resources for several users at the same time, which can therefore not be transmitted in
several directions simultaneously unless using several beams. In the case where beams would be sent in
all directions, the transmit power would be shared among the different beams; this is likely to lead to the
same performance as omni-directional transmission, without reduction of the interference for the
neighbouring cells. On the other hand, sending beams only to the users requesting MBMS would add
signalling requirements to the UTs in order to report channel knowledge, in case of GoB their best beam.
The interplay between beamforming and the scheduling strategy has also been studied. It has been shown
that when used in combination with proportional fair scheduling, the interference reduction and
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throughput gain comes first from proportional fair scheduling, which exploits the channel variations at the
users’ receiver, and then from the beamforming.
Receive diversity and especially IRC at the UT receiver provide significant gains (about 15 % of
additional sector throughput in non-adaptive mode) and therefore should be implemented if the
processing capabilities of the UT allow it. In the uplink, the efficiency of IRC is much reduced compared
to the downlink due to the high number of interfering sources which tends to whiten the interference, but
still allows significant sector throughput gains (+5 % and +10 % w.r.t. MRC for two and four antennas,
respectively). An implementation study has shown that the interpolation of the IRC coefficients yields the
same performance as their computation from interpolated interferers’ channel coefficients, but with the
advantage of significant savings in complexity. This approach should be consequently preferred.
However, a question that remains is how the gains reported in the studies (performed under the
assumption of perfect channel and interference correlation matrix knowledge) will be affected by
estimation errors in the IRC coefficients computation. We consequently recommend that further studies
assess the sensitivity of this estimation to the system performance.
The frequency adaptivity has been shown to generally lower the relative gains brought by IRC at the UT
and GoB at the BS, but these gains still remain significant (approximately +10 % in sector throughput for
both techniques, +150 % and +370 % in cell edge throughput, for IRC and GoB, respectively). This is due
in part to the robustness provided by the frequency adaptivity w.r.t. inter-cell interference. In the special
case of GoB, an additional reason is the interference variability induced by the time slot based beam
allocation, which affects the link adaptation reliability especially at the cell edge. With this respect, the
ability of IRC to efficiently mitigate a small number of interfering sources allows the fluctuations in the
interference level to be smoothed, thus partly recovering the link adaptation degradation.
IRC can be applied in conjunction with any other interference mitigation method. In particular, IRC at the
UT and GoB at the BS have been shown to complement very well each other, especially because the
interference typically is dominated by one strong interferer when the UT is hit by an interfering beam,
which is a highly favourable environment for the IRC. In terms of spectral efficiency, it has been shown
that GoB at the BS and dual antenna IRC at the UT gives 3 bps/Hz/sector, compared to 1 bps/Hz/sector
for the SISO case. Moreover, IRC can also be applied with macro diversity techniques at the transmitter
without any restriction.
In addition, IRC can be nicely combined with interference cancellation [WIN2D471], providing in
particular additional processing gain to aid separating the useful signal from the interference. An
additional gain of 33 % in cell edge throughput has been reported in [WIN2D471] for a single transmit
antenna and 2 receive antennas with perfect cancellation of the dominant interferer. Similarly, in
[WIN2D472] IRC has been shown to still bring performance improvements when used in addition to
interference avoidance techniques, where additional gains of 8 % in sector throughput can be observed.
However, the gain provided over MRC in case of combination with interference cancellation or avoidance
is reduced due to the lowered interference level resulting from the complementary mitigation scheme.
Contrary to IRC at the UT, GoB at the BS has been shown not to benefit from the association with
interference coordination schemes by means of transmit power restrictions, whose aim is to create subbands
with a reduced interference level. What is more, interference coordination even degrades the GoB
performance in the frequency-adaptive mode [WIN2D472]. This surprising behaviour can be explained
by the fact that the interference reduction in such coordination methods is achieved at the expense of a
transmit power reduction in other resources. The GoB, because of the directivity of the interference,
dynamically creates almost interference-exempt resources when the UT is not hit by an interfering beam.
As a consequence, the GoB has very little to gain from sub-bands with lower interference levels (the
actual gain is expected to depend on the probability to be interfered by a beam versus the probability to be
scheduled in resources with reduced interference, as well as the effective interference reduction in the
latter). On the contrary, the reduced power on the other sub-bands affects the useful signal power received
on these sub-bands, which effectively reduces the efficiency of the GoB compared to full resource re-use
in all cells.
The next step of the interference mitigation work within WINNER is to bring these recommendations for
smart antenna based interference mitigation together with those for interference averaging and
interference avoidance reported in [WIN2D471] and [WIN2D472] respectively, and further identify
possible combinations of methods in order to come up with a complete and adaptive inter-cell
interference mitigation strategy for the WINNER system.
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6. Conclusions
In this deliverable it has been investigated how smart antennas can be used to mitigate inter-cell
interference. The starting point was the multiple antenna concept developed during WINNER phase I
[WIN1D27][WIN1D210], and further refined in WINNER phase II [WIN2D341]. First it was identified
how this concept can be utilised for interference mitigation. The identified methods can be divided in
three categories: beamforming techniques, transmit diversity techniques, and receive diversity /
interference suppression techniques. These methods were then investigated in terms of what requirements
they put on the system concept regarding e.g. architecture, measurements and signalling, and assessed
from a performance point of view. The performance assessments were mainly carried out via computer
simulations. Most of the simulations were performed on system level, but some aspects were also studied
with link level and multi-link simulations.
The conclusion is that it is possible to significantly improve the robustness to inter-cell interference by
using smart antennas. Transmit beamforming, for example in the form of the WINNER wide area
baseline scheme fixed Grid of Beams (GoB), is an efficient means to reduce the interference spread in the
system, and in particular to protect users at the cell border from inter-cell interference. With SDMA on
top of GoB, the system performance is improved at the expense of slightly less protection of the cell edge
users. Also the use of multi antenna receivers at both base stations and user terminals has been shown to
be efficient means to mitigate interference. This allows implementation of receive diversity combining
schemes such as Maximum Ratio Combining (MRC) and spatial interference suppression schemes such
as Interference Rejection Combining (IRC). Already MRC provides considerable improvements in
interference tolerance both when used at user terminals in downlink and at base stations in uplink.
Additional improvements are achieved with IRC, in particular for downlink reception at user terminals. In
this context it should be mentioned that IRC is a more complex method than MRC, but studies on how
this complexity can be reduced thereby saving e.g. terminal battery life have also been conducted. For
downlink, the combination of transmit beamforming at the base stations and multi antenna reception with
IRC at user terminals, has been identified as an attractive combination. Furthermore, different aspects of
macro diversity, i.e. transmit diversity from several base stations, have been studied. For example,
different receive combining methods for SFN and MFN networks in conjunction with MBMS were
investigated. In addition, one macro diversity method based on cyclic delay diversity (CDD) was shown
to have potential to further improve the inter-cell interference situation at cell border areas.
Some work has also been spent on how the smart antenna based interference mitigation methods can be
combined with interference averaging and interference avoidance methods. However, these studies have
considered only few of the averaging and avoidance methods developed in [WIN2D471] and
[WIN2D472], respectively. Therefore, further work is needed in this area in order to come up with a
complete and adaptive inter-cell interference mitigation strategy for the WINNER system. This will be
the focus of the work on interference mitigation during the remainder of WINNER phase II.
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7. References
[3GPP25214] 3GPP TS 25.214 Physical Layer Procedures (FDD) (Release 7), V7.0.0 (2006-03)
[3GPP25913] 3GPP TR 25.913 Requirements for Evolved UTRA (E-UTRA) and Evolved UTRAN
(E-UTRAN) (Release 7), V7.3.0 (2006-03)
[3GPP36211] 3GPP TS 36.211 Physical channels and Modulation (Release 8), Nov. 2006
[AK05] G. Auer and E. Karipidis, “Pilot Aided Channel Estimation for OFDM: a Separated
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[CBB+01]
[DK01]
[Dol46]
[DT02]
[HBD00]
[IFN02]
[OSB98]
[PD06]
[Vau88]
[WIN1D27]
[WIN1D210]
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A. Dammann and S. Kaiser, “Standard conformable antenna diversity techniques for
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C.L. Dolph, “A current distribution for broadside arrays which optimises the
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M. Dong and T. Lang, “Optimal Design and Placement of Pilot Symbols for Channel
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M. Inoue, T. Fujii, and M. Nakagawa, “Space time transmit site diversity for OFDM
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O. Edfors, M. Sandell, J.-J. van de Beek, S. K. Wilson and P. O. Börjesson, “OFDM
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Fall), Montreal, Canada, Sep. 2006
R. G. Vaughan, "On optimum combining at the mobile," IEEE Trans. Veh. Technol.,
vol. 37, no. 4, Nov. 1988
IST-2003-507581 WINNER, “D2.7 Assessment of advanced beamforming and MIMO
technologies”, Feb. 2005
IST-2003-507581 WINNER, “D2.10 Final report on identified RI key technologies,
system concept, and their assessment”, Nov. 2005
[WIN2D111] IST-4-027756 WINNER II, “D1.1.1 WINNER II Interim Channel Models”, version 1.2,
Feb. 2007
[WIN2D341]
[WIN2D461]
IST-4-027756 WINNER II, “D3.4.1 The WINNER II Air Interface: Refined spatialtemporal
processing solutions”, Nov. 2006
IST-4-027756 WINNER II, “D4.6.1 The WINNER II Air Interface: Refined multiple
access concepts”, Nov. 2006
[WIN2D471] IST-4-027756 WINNER II, “D4.7.1 Interference averaging concepts”, June 2007
Page 40 (97)

WINNER II D4.7.3 v1.0
[WIN2D472] IST-4-027756 WINNER II, “D4.7.2 Interference avoidance concepts”, June 2007
[WIN2D6131]
[WIN2D6137]
[Win84]
[ZK01]
IST-4-027756 WINNER II. “D6.13.1 WINNER II Test Scenarios and Calibration Cases
Issue 1”, June 2006
IST-4-027756 WINNER II. “D6.13.7 WINNER II Test Scenarios and Calibration Cases
Issue 2”, Dec. 2006
J. H. Winters, “Optimum combining in Digital Mobile Radio with Cochannel
Interference”, in IEEE Journal on Selected Areas in Communications, Vol. 2, No. 4,
July 1984
J. Zander and S.-L. Kim, “Radio resource Management for Wireless Networks”, Artech
House, 2001
Page 41 (97)

WINNER II D4.7.3 v1.0
Appendix A.
Inter-cell interference modelling
In order to perform efficient investigations of inter-cell interference mitigation schemes, whilst still
producing results from which valid conclusions can be drawn, it is important to understand the impact
caused by different numbers and locations of interfering sources. Therefore, simulation studies have been
carried out to measure the inter-cell interference experienced by a victim UT under a number of different
scenarios, and how this varies with different numbers of interference sources modelled accurately, as
Gaussian noise, or not at all.
A.1 Scenarios
Since the studies of inter-cell interference mitigation by smart antennas are carried out within the Wide
Area Base Coverage Urban scenario [WIN2D6137], the same system parameters and network
deployment model are considered here.
The central cell technique is used to study the victim UT, and interfering signals from all other BS are
each passed through independent WINNER C2 channel models [WIN2D111] to produce the separate
interferers reaching the victim UT. It is assumed that the channel is constant within a chunk, and a large
number of uncorrelated snapshots are modelled. Results are produced for UT located at cell centre, cell
edge and sector border, with the simulation layouts shown in Figures A-1 to A-3. Two tiers of interfering
BS sites, each with 3 sectors, are modelled, with the addition of an extra layer of sites to simulate the
most significant sites of a third tier in the case where the UT is not centrally located within a cell.
Studies are carried out for both single antenna UT and UT with two antennas. BSs are modelled as
transmitting at full power with an omni pattern within a sector, which is equivalent to equal use of all
beams in a GoB when fully loaded.
3000
1000
800
2000
600
Cells area (m)
1000
0
−1000
Cells area (m)
400
200
0
−200
−400
−2000
−600
−3000
−3000 −2000 −1000 0 1000 2000 3000
Cells area (m)
−800
−1000
−1000 −500 0 500 1000
Cells area (m)
Figure A-1: Cell centre UT. UT located 50 m from BS.
3000
1000
800
2000
600
Cells area (m)
1000
0
−1000
Cells area (m)
400
200
0
−200
−400
−2000
−600
−3000
−3000 −2000 −1000 0 1000 2000 3000
Cells area (m)
−800
−1000
−1000 −500 0 500 1000
Cells area (m)
Figure A-2: Cell edge UT. UT located 660 m from BS.
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WINNER II D4.7.3 v1.0
3000
1000
800
2000
600
Cells area (m)
1000
0
−1000
Cells area (m)
400
200
0
−200
−400
−2000
−600
−3000
−3000 −2000 −1000 0 1000 2000 3000
Cells area (m)
−800
−1000
−1000 −500 0 500 1000
Cells area (m)
Figure A-3: Sector border UT. UT located 160 m, at an angle pi/3 radians from sector antenna
array broadside.
It can be seen that these layouts include between 24 and 27 BS sites, offering 72-81 links which could be
simulated.
A.2 Results
A.2.1 Number of significant links
In these results, we explore the number of links which should be modelled to produce sufficient accuracy
of interference modelling. Full results are only available for the single antenna UT case, however
available results for the dual antenna UT show similar performance.
Firstly we consider the error in received per chunk SINR imparted by ignoring those links contributing
least power to the total interference, see Figure A-4.
mean of SINR per chunk error compared to 72 links (dB)
0.2
0
−0.2
−0.4
−0.6
−0.8
−1
−1.2
−1.4
−1.6
Single antenna UT. Unsimulated links: all rx power ignored
−1.8
Cell centre UT (radius 50m)
Cell edge UT (radius 660m)
−2
0 10 20 30 40 50 60 70 80
Number of accurately modelled links
std of SINR per chunk error compared to 72 links (dB)
2
1.8
1.6
1.4
1.2
1
0.8
0.6
0.4
0.2
0
Single antenna UT. Unsimulated links: all rx power ignored
Cell centre UT (radius 50m)
Cell edge UT (radius 660m)
0 10 20 30 40 50 60 70 80
Number of accurately modelled links
Figure A-4: Impact of ignoring links.
It can be seen that including ~30 links at cell edge, and ~15 links at cell centre will produce results with
less than 0.1 dB error compared to full modelling. Relaxing the permissible error to 0.5 dB reduces the
number of required links to only ~10 at cell edge and ~4 at cell centre.
Having identified that we can safely ignore a substantial proportion of the possibly interfering links, the
next approach is to replace accurately modelled links with a Gaussian noise source with the same long
term power as the replaced link, see Figure A-5.
Page 43 (97)

WINNER II D4.7.3 v1.0
mean of SINR per chunk error compared to 72 links (dB)
Single antenna UT. Unsimulated links: rx power modelled as white noise
0.8
Cell centre UT (radius 50m)
0.7
Cell edge UT (radius 660m)
0.6
0.5
0.4
0.3
0.2
0.1
0
−0.1
0 10 20 30 40 50 60 70 80
Number of accurately modelled links
std of SINR per chunk error compared to 72 links (dB)
Single antenna UT. Unsimulated links: rx power modelled as white noise
2
Cell centre UT (radius 50m)
1.8
Cell edge UT (radius 660m)
1.6
1.4
1.2
1
0.8
0.6
0.4
0.2
0
0 10 20 30 40 50 60 70 80
Number of accurately modelled links
Figure A-5: Impact of modelling links by noise.
Here it can be seen that once we have modelled a sufficient number of links to approach within 0.5 dB of
full performance, any remaining links required to achieve better accuracy can be modelled by Gaussian
noise.
Results for sector border UTs are unsurprisingly between the bounds created by the cell centre and cell
edge cases.
A.2.2 Identification of significant links
In Figures A-6 to A-8, we show the probability that particular links can be ignored or replaced with
Gaussian noise without significantly impacting the inter-cell interference received at the victim UT. In
this case we consider the probability that ignoring or replacing a particular interference source will
contribute at least a 0.1 dB error in the received inter-cell interference power. Results are only presented
for the dual antenna UT case, although again there is similarity of results between the single and dual
antenna cases.
Two−antenna cell−edge UT: Probability rx power can be ignored without significantly affecting SINR
Two−antenna cell−edge UT: Probability rx power can be replaced by WGN without significantly affecting SINR
0.94
0.98
Cells area (m)
3000
2000
1000
0
−1000
−2000
0.99
0.82
0.89
0.96
0.88
0.89
0.62 0.63
0.97
0.83
0.9
0.83 0.21 0.24 0.85
0.62
0.6
0.24 0.23 0.23 0.16 0.9
0.82
0.03
0.89
0.65 0.04 0.04 0.61
0.22
0.15
0.11 0 0 0.11 0.9
0.53
0
0.53
0.49 0.02 0.01 0.28
0.14
0.14
0.31 0.15 0.16 0.27 0.92
0.4
0.06
0.46
0.84 0.71 0.69 0.81
0.21
0.23
0.88 0.87 0.86 0.78 0.99
0.32
0.95 0.94 0.96 0.94
0.99 0.97
Cells area (m)
3000
2000
1000
0
−1000
−2000
1
0.92
0.93
0.99
0.97
0.95
0.77 0.74
1
0.93
0.97
0.93 0.41 0.35 0.91
0.77
0.77
0.35 0.37 0.36 0.33 0.98
0.91
0.08
0.94
0.77 0.04 0.07 0.79
0.42
0.24
0.29 0 0 0.26 0.94
0.79
0
0.73
0.65 0.06 0.06 0.53
0.27
0.25
0.44 0.41 0.4 0.45 0.96
0.63
0.18
0.58
0.93 0.86 0.8 0.93
0.41
0.45
0.95 0.91 0.91 0.92 1
0.51
0.98 0.98 0.99 0.99
1 1
−3000
−3000
−3000 −2000 −1000 0 1000 2000 3000
Cells area (m)
−3000 −2000 −1000 0 1000 2000 3000
Cells area (m)
Figure A-6: Probability links can be ignored for cell edge UT.
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WINNER II D4.7.3 v1.0
Two−antenna cell−centre UT: Probability rx power can be ignored without significantly affecting SINR
Two−antenna cell−centre UT: Probability rx power can be replaced by WGN without significantly affecting SINR
1
0.99
Cells area (m)
3000
2000
1000
0
−1000
−2000
1
0.98
0.98
0.99
0.99
1
0.93 0.93
1
0.99
0.98
0.99 0.79 0.79 0.99
0.97
1
0.75 0.9 0.88 0.66 0.98
0.98
0.89
1
0.98 0.48 0.47 1
0.84
0.86
0.59 0.35 0.34 0.54 1
0.96
0
0.99
0.88 0.14 0.09 0.87
0.42
0.45
0.44 0 0 0.52 0.99
0.87
0.09
0.84
0.92 0.51 0.53 0.88
0.5
0.52
0.93 0.92 0.91 0.88 1
0.55
0.99 0.98 0.99 0.98
0.99 0.99
Cells area (m)
3000
2000
1000
0
−1000
−2000
1
0.96
0.94
0.97
0.99
0.99
0.89 0.87
1
0.98
0.98
0.95 0.64 0.61 0.95
0.88
0.93
0.56 0.74 0.72 0.45 0.98
0.94
0.7
0.94
0.91 0.32 0.3 0.93
0.71
0.67
0.37 0.19 0.2 0.31 0.95
0.9
0
0.95
0.72 0.02 0.06 0.68
0.24
0.22
0.27 0 0 0.32 0.96
0.65
0.01
0.64
0.8 0.32 0.31 0.78
0.27
0.27
0.72 0.8 0.78 0.72 0.98
0.38
0.94 0.94 0.93 0.92
0.99 0.99
−3000
−3000
−3000 −2000 −1000 0 1000 2000 3000
Cells area (m)
−3000 −2000 −1000 0 1000 2000 3000
Cells area (m)
Figure A-7: Probability links can be ignored for cell centre UT.
Dual receiver antenna Sector−border UT: Probability rx power can be ignored without significantly affecting SINR
Dual receiver antenna Sector−border UT: Probability rx power can be replaced by WGN
without significantly affecting SINR
0.97
0.99
Cells area (m)
3000
2000
1000
0
−1000
−2000
1
0.92
0.91
0.96
0.97
0.97
0.83 0.76
1
0.96
0.93
0.87 0.49 0.47 0.85
0.84
0.83
0.98
0.39 0.68 0.44 0.34 0.93
0.89
0.5
0.94
0.85 0.14 0.19 0.76 0.39 0.98
0.61
0.34
0.97
0.23 0.16 0.08 0.13 0.94
0.88
0
0.83
0.61 0.02 0
0.3 0.24 0.97
0.21
0.09
0.87
0.24 0
0 0.12 0.92
0.61
0.01
0.43
0.69 0.22 0.17 0.58 0.51 0.98
0.19
0.11
0.61 0.67 0.66 0.54 0.92
0.28
0.91 0.91 0.89 0.89
0.96 0.96
Cells area (m)
3000
2000
1000
0
−1000
−2000
1
0.96
0.98
0.99
0.98
0.99
0.91 0.89
1
0.98
0.98
0.97 0.68 0.68 0.95
0.96
0.97
0.99
0.61 0.85 0.71 0.48 0.98
0.96
0.73
0.95
0.96 0.32 0.32 0.95 0.62 0.99
0.8
0.56
1
0.44 0.29 0.15 0.34 0.96
0.95
0
0.95
0.82 0.06 0.01 0.62 0.49 1
0.38
0.18
0.97
0.36 0 0 0.29 0.96
0.81
0.04
0.62
0.9 0.4 0.3 0.79 0.73 0.99
0.4
0.29
0.84 0.86 0.86 0.72 1
0.42
0.98 0.94 0.95 0.95
0.98 0.98
−3000
−3000
−3000 −2000 −1000 0 1000 2000 3000 4000
Cells area (m)
−3000 −2000 −1000 0 1000 2000 3000 4000
Cells area (m)
Figure A-8: Probability links can be ignored for sector border UT.
As with the previous results it can be seen that there are a comparatively small number of highly
significant interfering links, a number of links which are significant but may be replaced with Gaussian
noise, and a number of relatively insignificant links.
What is important to note is that the direction of sectorised cells is important, and that the significant links
are not always from the closest sites. Additionally, it is clear that inter-cell interference between different
cells of the same BS site is significant, despite the diverging antenna array directions.
Page 45 (97)

WINNER II D4.7.3 v1.0
Appendix B. Downlink interference mitigation with multiple
antennas in non-frequency adaptive networks
B.1 Description
This study will investigate and assess the use of multiple antennas for downlink inter-cell interference
mitigation in the spatial domain.
The multiple antennas at the BS will be used for transmit beamforming, and in this study we will use the
so-called Grid-of-Beams (GoB) scheme, that is part of the WINNER baseline design [WIN2D6137]. The
GoB scheme is a fixed beamforming scheme, i.e. the transmission takes place in pre-defined beams
pointing in certain directions. The best beam for downlink transmission for each user is selected on a
long-term basis. This differs from adaptive beamforming approaches where the beams are formed
adaptively in order to optimise the transmission. It has, however, in several studies been shown that the
gain of going from fixed beamforming to adaptive beamforming is rather limited, see e.g. [WIN2D341].
Beamforming is known to be an efficient means to mitigate inter-cell interference, since by transmitting
in narrow beams the interference spread to other cells is significantly reduced, which improves the system
performance.
At the UT, the multiple receive antennas are used to implement combining schemes in the baseband
signal processing. Since the radio channels (from a transmit antenna) to the receive antennas tend to fade
differently, multi antenna receivers provide diversity – both for the signal of interest and for the
interference. With appropriate selection of the antenna combining weights, accounting for e.g. the radio
channel, the interference power and the spatial colouring of the interference, such multi antenna receivers
may provide increased robustness to both fading and interference. This, in turn, may improve the radio
network coverage, capacity and user data rates. Maximum ratio combining (MRC) and interference
rejection combining (IRC) are two well-known combining schemes. With MRC the combining weights
are selected accounting for the radio channel (of the desired signal), the noise power and the interference
power at the different receive antennas. IRC, sometimes also referred to as optimal combining [Win84] or
minimum mean square error (MMSE) combining, determines the combining weights based on the
channel and the (spatial) noise and interference covariance matrix, i.e., not only the interference power
but also the spatial colouring of the interference is taken into account.
Beamforming by itself, as well as IRC by itself, provides increased robustness to inter-cell interference.
Of particular interest is, however, the combination of the two methods; i.e. to use beamforming in the BS
combined with IRC (or other baseband interference rejection schemes) in the UTs. These methods have
earlier been demonstrated to complement each other very well and provide almost additive gains, e.g. for
GSM in [CBB+01]. The reason for this is that most baseband interference rejection methods, e.g. IRC,
are designed to be very efficient in the case where there is one strongly dominating interfering source, and
that beamforming, i.e. to transmit in narrow beams in certain directions, creates an interference
environment that very often is dominated by one single interferer.
No coordination between the BSs will be used, i.e. we will not try to avoid scheduling of users in
different cells in beams pointing to the same area. In other words, beam collisions may occur, but in that
case we will rely on the interference rejection capability that IRC provides.
B.2 Scenarios
The GoB scheme is in principle possible to use in any scenario. It is in [WIN2D6137] defined as baseline
design both in WA and MA, but with different number of beams.
Both MRC and IRC are baseband receive processing schemes, and is therefore independent of scenario.
B.3 Requirements
The GoB scheme has very low requirements on measurements and signalling. The requirement for beam
selection for downlink transmission is that each user signals back its best beam index on a long-term
basis. Since we do the beamforming uncoordinated between different cells, we do not require any inter-
BS communication.
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WINNER II D4.7.3 v1.0
The interference rejection gain provided by IRC depend on that accurate channel and interference
estimates are available at the receiver and it is hence essential to consider such aspects in the pilot pattern
design. It should also be mentioned that for IRC it is advantageous with time synchronised cells, but it is
not a requirement; IRC will still work but the gain will not be maximised.
B.4 Evaluations
The evaluations are performed as computer simulations of a non-frequency adaptive OFDMA/TDMA
network, i.e. system level simulations. The studied deployment comprises 19 sites, each with three sectors
(cells) per site. This means that the base coverage urban scenario, defined in [WIN2D6137] as a WA
scenario, is part of the study.
B.4.1 Assumptions
The simulation assumptions follows to a large extent the deployment scenario and system parameters as
specified for the base coverage urban scenario, defined in [WIN2D6137] as a WA scenario. The studied
deployment comprises 19 sites, each with three sectors (cells) per site. Each sector is equipped with an
antenna array comprising four elements separated half a wavelength. The antenna array is in the downlink
used to form 8 fixed beams, i.e. the baseline GoB scheme in [WIN2D6137] is implemented. The beam
selection for downlink transmission is done on a long term basis in a way that the beam associated with
the lowest pathloss is used. The user terminals have two or four antennas separated half a wavelength and
MRC or IRC is employed on a per sub-carrier basis. Cross polarisation as specified in [WIN2D6137] is
not modelled. As reference cases, single transmit antennas (sector-covering) at the base stations and
single receive antennas at the user terminals are simulated.
Round robin TDMA scheduling is used, i.e., in each frame a single user per sector is assigned to the entire
transmission bandwidth of 40 MHz. The transport format (modulation scheme and channel code rate) is
selected to maximise the expected throughput. There are three available modulation schemes (QPSK,
16QAM, and 64QAM) and six different channel code rates (1/10, 1/3, 1/2, 2/3, 3/4, and 8/9).
The simulations assume perfect channel and interference estimation at the receiver. Similarly, channel
quality measurement errors and delays are not accounted for. The OFDM transmission is further modelled
as perfectly orthogonal and any potential inter-symbol or inter-carrier interference caused by channel time
dispersion exceeding the cyclic prefix is neglected. Overhead such as pilots, e.g. for channel and
interference estimation, or protocol headers are neither accounted for. A summary of some important
simulation parameters are given in Table B-1 below.
Table B-1: Simulation assumptions.
Channel model
C2
Number of base stations 57
Number of users per cell 12 in average
Site-to-site distance 1000 m
Wrap-around
Yes
Interference modelling All links modelled
BS antenna configuration 4-element ULA, antenna element separation 0.5λ
BS antenna gain 14 dBi
UT antenna configuration 2 and 4-element ULA, antenna element separation 0.5λ
UT antenna gain 0 dBi
UT velocity
50 km/h
Coding
Rate 1/3 turbo code with rate matching for rates
1/10, 1/3, ½, 2/3, 3/4, 8/9
Modulation
QPSK, 16QAM, 64QAM
Link adaptation
Ideal
Retransmission / HARQ No
Multiple access
B-EFDMA
Scheduling
Round robin TDMA
Page 47 (97)

WINNER II D4.7.3 v1.0
The used performance measures are the post receiver SINR, the active radio link data rate and the average
sector throughput. The first two may be described as user centric performance measures while the last is
focused on the system performance. The post receiver SINR is the symbol SINR after antenna combining
(geometrically) averaged over all symbols in the frame (code block). The active radio link data rate is the
user data rate when scheduled for transmission averaged over all transmission attempts. The average
throughput, here measured in Mbps/sector, is calculated as the number of correctly received bits in
relation to simulation time and the number of sectors.
B.4.2 Results
Figure B-1 below shows CDFs of post receiver SINR. It can be seen that terminal dual antenna reception
with MRC enhances the average SINR by around 3 dB for almost all users and IRC gives an additional
improvement of approximately 1.5 dB, compared to the SISO case. It can also be seen that GoB
transmission improves the SINR by about 7 dB compared to SISO. However, of particular interest are the
results of GoB transmission in combination with terminal dual antenna reception; with MRC the gain
over SISO is almost around 10.5 dB, and with IRC additional 2 dB, i.e. about 12.5 dB. Hence, the
improvements in post receiver SINR of terminal dual antenna reception and GoB transmission are more
or less additive.
100
90
80
70
C.D.F. [%]
60
50
40
30
20
10
M TX
= 1, M RX
= 1
M TX
= 1, M RX
= 2: MRC
M TX
= 1, M RX
= 2: IRC
M TX
= 4, M RX
= 1: GOB
M TX
= 4, M RX
= 2: GOB + MRC
M TX
= 4, M RX
= 2: GOB + IRC
0
-20 -10 0 10 20 30 40 50
SINR [dB]
Figure B-1: CDFs of average post receiver SINR.
A perhaps more interesting performance measure is depicted in Figure B-2, where CDFs of active radio
link rate, i.e. user data rate when transmitting, are shown. As can be expected from the improvements in
SINR discussed above, it can be seen that both dual antenna reception and GoB transmission significantly
enhance the active radio link rate. For example, if we look at the 10-percentile we can see that SISO gives
an active radio link rate of approximately 9 Mbps, while terminal dual antenna reception with MRC gives
about 17 Mbps and with IRC around 20 Mbps. GoB transmission provides 30 Mbps and combined with
dual antenna MRC and IRC it gives as much as 47 Mbps and 55 Mbps, respectively. These are significant
improvements; just by adding dual antenna MRC reception in the terminals the 10-percentile of the active
radio link rate is doubled, and with GoB transmission and IRC in terminals the increase is more than 500
%. Since the 10-percentile corresponds to the users with worst conditions, most probably located at the
cell borders, this measure can in some sense be interpreted as a measure of the coverage. The results are
summarised in Figure B-3 below, where also results for four antenna reception with MRC and IRC are
added. One interesting observation that can be made is that the relative gain of IRC compared to MRC is
larger with four antennas than with two antennas, 30 % and 17 % respectively.
The impact on system capacity is summarised in, Figure B-4, which shows the average sector throughput.
With SISO the average sector throughput reaches almost 40 Mbps, which corresponds to around 1
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WINNER II D4.7.3 v1.0
bps/Hz/sector. Introducing terminals with dual receive antennas using MRC increases the average
throughput by 38 %, while IRC gives additional 22 %. By going from SISO to GoB transmission the
throughput is doubled, and by combining it with dual antenna MRC and IRC the gain over SISO is 155 %
and 184 %, respectively. This means that with GoB transmission and terminal dual antenna reception with
IRC it is possible to reach almost 3 bps/Hz/sector. Also here we see significant gains of increasing the
number of receive antennas to four, and that the gain of IRC versus MRC is larger than for two receive
antennas.
100
90
80
70
C.D.F. [%]
60
50
40
30
20
10
M TX
= 1, M RX
= 1
M TX
= 1, M RX
= 2: MRC
M TX
= 1, M RX
= 2: IRC
M TX
= 4, M RX
= 1: GOB
M TX
= 4, M RX
= 2: GOB + MRC
M TX
= 4, M RX
= 2: GOB + IRC
0
0 20 40 60 80 100 120 140 160 180 200 220
User data rate when transmitting [Mbps]
Figure B-2: CDFs of active radio link rate (user data rate when transmitting).
10th perc. user data rate (when transmitting) [Mbps]
100,0
90,0
80,0
70,0
60,0
50,0
40,0
30,0
20,0
10,0
0,0
SISO
MRC(2)
IRC(2)
MRC(4)
IRC(4)
GOB
GOB+MRC(2)
GOB+IRC(2)
GOB+MRC(4)
GOB+IRC(4)
Figure B-3: 10 th percentile of the active radio link rate (user data rate when transmitting).
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WINNER II D4.7.3 v1.0
160,0
average sector throughput [Mbps/sector]
140,0
120,0
100,0
80,0
60,0
40,0
20,0
0,0
SISO
MRC(2)
IRC(2)
MRC(4)
IRC(4)
GOB
GOB+MRC(2)
GOB+IRC(2)
GOB+MRC(4)
GOB+IRC(4)
Figure B-4: Average sector throughput.
To summarise, we have seen that both transmit beamforming and terminal multiple antenna reception are
efficient means to mitigate inter-cell interference. The results indicate that GoB transmission provides
100 % capacity gain and 200 % coverage gain, and that dual antenna reception with MRC offers almost
40 % capacity gain and nearly 100 % coverage gain. By using IRC the gain is even larger. Increasing the
number of receive antennas to four gives further gains that are significant, indicating that this might be a
very interesting alternative for devices that can accommodate the antennas, e.g. laptop computers. Worth
noting is that the two techniques (transmit beamforming and terminal multiple antenna reception)
complements each other very well. For example, GoB transmission and terminal dual antenna reception
with IRC give about 180 % capacity gain and more than 500 % coverage gain. The reasons for this are
two; first, the GoB transmission reduces the inter-cell interference spread in the network significantly
since the probability that neighbouring base stations direct their transmissions in the same direction is
significantly reduced. Secondly, when this still happens the interference is most probably strongly
dominated by a single source, which is exactly the situation that most interference suppression techniques
for dual receive antennas are designed for. In other words, the transmit beamforming creates an
interference environment that is particularly favourable for e.g. IRC. It should, however, be noted that
these results will change if SDMA is implemented on top of the GoB scheme, since that means that the
same time-frequency resources will be transmitted in different beams at the same time. This implies that
the strong directivity property of the interference will be reduced, meaning that the probability for beam
collisions increases as well as the probability for more interfering sources which reduces the efficiency of
e.g. IRC.
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WINNER II D4.7.3 v1.0
Appendix C. Downlink interference mitigation with multiple
antennas in frequency-adaptive and non-adaptive networks
C.1 Description of the considered mitigation techniques
In this study we consider two spatial combining schemes at the UT, the Maximum Ratio Combining
(MRC) and the Interference Rejection Combining (IRC). In addition, we consider two transmit schemes:
a single antenna at the transmitter and the baseline Grid of Beams (GoB) [WIN2D6137] with 4 transmit
antennas. First, we recall the principle of the MRC and IRC.
C.1.1 Maximum Ratio Combining (MRC)
The MRC combines coherently the signals output by the various sensors, by applying weights depending
on the signal to noise ratio (SNR) at each antenna output. As the true MRC requires the estimation of the
noise power, the weights are generally approximated by being only proportional to the strength of the
desired signal on each antenna, provided by the corresponding channel estimate. MRC provides diversity
gain provided the fades experimented on each receive antenna are sufficiently decorrelated. In addition,
MRC provides coherent combining gain as the desired signals are added coherently but not the
interference. Note that when interference is spatially white, MRC provides optimal performance
according to the maximisation of the signal to interference and noise ratio (SINR).
Figure C-1 illustrates the principle of spatial combining.
Spatial combining
*
w
1
samples received
from antenna 1
samples received
from antenna 2
symbol
estimates
*
w
2
Figure C-1: Principle of spatial combining in the case of two receive antennas.
In OFDMA, the combining has to be performed on a sub-carrier basis, i.e. the combining weights depend
on the sub-carrier. In the case of M R receive antennas, the (approximated) MRC combining weights on a
given sub-carrier are given by
T
w = [ w 1,
w2,
K , w M R
] = h
where h is the M R x 1 vector containing the channel coefficients of the signal of interest for the
considered sub-carrier.
C.1.2 Interference Rejection Combining (IRC)
The IRC accounts for the spatial structure of the interference, which allows the interference to be partly
rejected in the spatial domain [Vau88]. A terminal equipped with M T receive antennas can perfectly reject
M T -1 interfering sources, provided the interfering signals are received with different spatial signatures,
i.e. different directions of arrival (DoA), compared to the signal of interest. When the number of sources
are greater than M T -1, which is usually the case in real-world systems, the IRC rejects as much
interference as possible with a linear processing, in a way that maximises the SINR at the receiver output.
The IRC weights are given by
⎪⎧
−1
Ps
R h
w IRC = ⎨ −1
⎪⎩ α Γ h
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WINNER II D4.7.3 v1.0
where P s is the transmit power of the signal of interest, R is the spatial correlation matrix of the received
signal, Γ is the correlation matrix of the interference + noise term, and α is a real positive factor which
can be omitted in the computation of the filter (as well as P s ). The direct computation of the IRC weights
consequently requires the estimation and inversion of either the correlation matrix of the received signal,
or the correlation matrix of the interference + noise term. As a consequence, the IRC receiver requires a
substantial increase of the receiver complexity w.r.t. the MRC.
Note that when the interference is spatially white and is equal on each antenna, matrix Γ is proportional
to the identity matrix and the IRC weights then reduce to the weights of the MRC (up to a real positive
scaling factor that has no influence on the performance).
C.2 Requirements
The GoB requires the UT to feedback its best beam. If only one beam is reported for the whole bandwidth
as it is assumed in WINNER so far, the additional feedback signalling overhead is low. The directivity of
the GoB prevents it from being used for common control channels (e.g. the BCH), which have to be
broadcasted over the whole cell. Therefore, the GoB can be applied only to data channels.
The practical efficiency of the IRC depends on the accuracy of the IRC weights, which relies in particular
on the estimation of the interference correlation matrix. This estimation can be performed according to
two methods. The first one is often called “parametric” since it relies on the knowledge of the underlying
structure of the correlation matrix. It involves estimating the channel and received power of each
interferer, and then building the correlation matrix (either one). This method requires means to estimate
the interferers’ channel, the most convenient being to be able to use the interferers’ pilots. The second
method estimates directly the autocorrelation matrix of the received signal, by means of average sample
products. In OFDMA the average can be done in time and/or frequency. A trade-off may have to be found
in the averaging time/frequency window, since the larger the window the more accurate the estimation,
but the less it accounts for the variations of the channel (in time or in frequency, depending on the
dimension over which is made the average). Nevertheless, this method does not require any knowledge
about the interferers’ pilots, since the correlations are computed from the received samples (in frequency,
after the FFT) directly. As a consequence, all the received samples of a chunk can be used for this
purpose.
C.3 Evaluations
The performance evaluations are carried out using a class III system-level simulator [WIN2D6131].
C.3.1 Assumptions
The scenario considered almost matches the Base coverage urban downlink scenario, whose parameters
are detailed in [WIN2D6137], except the following deviations;
• in the frequency non-adaptive mode, chunk-based resource allocation is used instead of the B-
EFDMA. However, like in the B-EFDMA, the allocated resources are regularly spaced apart
within the whole system bandwidth;
• in the frequency non-adaptive mode, the link adaptation is performed based on the average
channel quality on all the allocated chunks, i.e. there is no chunk-wise modulation adaptation;
• receive diversity at the UT is obtained through a linear antenna array instead of cross-polarised
antennas;
• the duo-binary turbo codes are used instead of the LDPC codes;
• variable FEC block sizes (max size in the order of 5100 bits) are used instead of fixed ones,
without modulation adaptation within a FEC block. One retransmission unit contains a single
FEC block only.
We consider a hexagonal deployment of 57 sectors with inter-site distance of 1000 m. The multi-cell
environment is accounted for using the central cell technique (see Section 4.1.1.2.1), which means the
UTs and system functions are effectively modelled in the three sectors of the central cell, the remaining
sectors being accounted for via simplified models. The inter-cell interference modelling involves the 56
interfering sectors, the channel of the 7 dominant interferers in a long-term basis being accurately
modelled, whereas the other interferers’ channels are modelled as SISO and single-path ones. All the
interfering BSs are assumed to transmit at full power. Moreover, all the resources are permanently
occupied in the interfering cells, which means the system operates at full load. For GoB simulations, the
interfering cells whose contribution to the interference is accurately modelled also use the GoB, whereas
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WINNER II D4.7.3 v1.0
the cells simulated in a simplified manner are assumed to transmit using a single transmit antenna. The
beam allocation in the interfering cells is drawn randomly in a uniform and independent manner for each
chunk, and changes at each time slot.
The total number of users in each sector is 24, which leads to 12 users in downlink transmission per half
frame using the FDD half-duplex scheme. Full Buffer traffic model is used for all the users, i.e. the users
always have data waiting for transmission. Both the frequency-adaptive and non-adaptive modes are
considered, using OFDMA with chunk-wise resource allocation for both modes. The users’ velocity is 3
km/h in frequency-adaptive mode and 50 km/h in non-adaptive mode. At the transmitter, either singleantenna
transmission is employed, or the baseline Grid of Beams [WIN2D6137] with four antennas. The
UT always uses two receive antennas. The scheduler uses the Score-Based algorithm in the frequencyadaptive
mode, and Round Robin in the non-adaptive mode. Nine users are scheduled simultaneously in
the 45 MHz bandwidth, which leads to 16 chunks per user which are not necessarily co-localised. HARQ
with chase combining is fully simulated with 4 retransmissions and explicit feedback of ACK/NACK
messages. Link adaptation is simulated with a one-frame delay for the CQI feedback, the latter being
assumed to be perfectly received. Ten modulation and coding schemes are used, with BPSK, QPSK,
16QAM and 64QAM modulations and duo-binary turbo codes with rates 1/2, 2/3 and 3/4.
In this study, we assume perfect estimation of the correlation matrix of the interference + noise term, as
well as of the channel of the user of interest.
C.3.2 Performance results
Three performance metrics have been considered: the average sector throughput, the cell-edge throughput
(measured at 5% of the user throughput CDF) and the (geometric) average SINR over the allocated
resources. The average sector throughput, the user throughput CDFs (together with an enlargement of the
cell-edge throughput region) and the average SINR CDFs obtained for the considered multiple antenna
techniques are presented graphically in Figure C-2, Figure C-3 and Figure C-4, respectively. The results
for the non-adaptive mode are on the left-hand side of the figures, while the results for frequency-adaptive
mode are on the right-hand side. Finally, Table C-1 and Table C-2 summarise the main results for the
non-adaptive and frequency-adaptive modes, respectively.
Average sector througthput (Mb/s)
160
140
120
100
80
60
40
20
Average sector througthput
1 Tx - 2Rx MRC
1 Tx - 2Rx IRC
4 Tx GoB - 2Rx MRC
4 Tx GoB - 2Rx IRC
Average sector througthput (Mb/s)
160
140
120
100
80
60
40
20
Average sector througthput
1 Tx - 2Rx MRC
1 Tx - 2Rx IRC
4 Tx GoB - 2Rx MRC
4 Tx GoB - 2Rx IRC
0
Technique
0
Technique
Figure C-2: Average sector throughput CDF for the non-adaptive (left) and frequency-adaptive
(right) modes.
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WINNER II D4.7.3 v1.0
Prob( av. user throughput < X)
1
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
CDF of average user throughput
1 Tx - 2Rx MRC
1 Tx - 2Rx IRC
4 Tx GoB - 2Rx MRC
4 Tx GoB - 2Rx IRC
Prob( av. user throughput < X)
1
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
CDF of average user throughput
1 Tx - 2Rx MRC
1 Tx - 2Rx IRC
4 Tx GoB - 2Rx MRC
4 Tx GoB - 2Rx IRC
0
0 1 2 3 4 5 6 7 8
Av. user throughput (Mb/s)
0
0 1 2 3 4 5 6 7 8
Av. user throughput (Mb/s)
0.1
CDF of average user throughput
0.1
CDF of average user throughput
0.09
0.09
Prob( av. user throughput < X)
0.08
0.07
0.06
0.05
0.04
0.03
0.02
0.01
1 Tx - 2Rx MRC
1 Tx - 2Rx IRC
4 Tx GoB - 2Rx MRC
4 Tx GoB - 2Rx IRC
0
0 0.5 1 1.5 2 2.5
Av. user throughput (Mb/s)
Prob( av. user throughput < X)
0.08
0.07
0.06
0.05
0.04
0.03
0.02
1 Tx - 2Rx MRC
1 Tx - 2Rx IRC
0.01
4 Tx GoB - 2Rx MRC
4 Tx GoB - 2Rx IRC
0
0 0.5 1 1.5 2 2.5
Av. user throughput (Mb/s)
Figure C-3: User throughput CDF for the non-adaptive (left) and frequency-adaptive (right)
modes.
1
CDF of the scheduled resource SINR
1
CDF of the scheduled resource SINR
0.9
0.9
0.8
0.8
Prob( SINR < X)
0.7
0.6
0.5
0.4
0.3
0.2
0.1
1 Tx - 2Rx MRC
1 Tx - 2Rx IRC
4 Tx GoB - 2Rx MRC
4 Tx GoB - 2Rx IRC
0
-5 0 5 10 15 20 25 30 35
SINR (dB)
Prob( SINR < X)
0.7
0.6
0.5
0.4
0.3
0.2
0.1
1 Tx - 2Rx MRC
1 Tx - 2Rx IRC
4 Tx GoB - 2Rx MRC
4 Tx GoB - 2Rx IRC
0
-5 0 5 10 15 20 25 30 35
SINR (dB)
Figure C-4: CDF of the average SINR over the allocated chunks for the non-adaptive (left) and
frequency-adaptive mode (right) modes.
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WINNER II D4.7.3 v1.0
Table C-1: Summary of results for frequency non-adaptive mode:
Scheme
Sector service throughput
(Mb/s)
Cell-edge service
throughput (kb/s)
Average SINR gain (dB)
(at 50% of the CDF)
1 Tx
MRC
1 Tx
IRC
GoB 4Tx
MRC
GoB 4Tx
IRC
47 54 74 84
+0% +16% +58% +80%
120 285 990 1100
+0% +140% +720% +810%
0 +1.1 +4.8 +6.2
Table C-2: Summary of results for frequency adaptive mode:
Scheme
Sector service throughput
(Mb/s)
Cell-edge service
throughput (kb/s)
Average SINR gain (dB)
(at 50% of the CDF)
1 Tx
MRC
1 Tx
IRC
GoB 4Tx
MRC
GoB 4Tx
IRC
86 94 95 105
+0% +9% +10% +21%
320 800 1500 1800
+0% +150% +370% +460%
0 +0.9 +3.5 +5.0
Consider first the adaptive mode. IRC at the UT increases the sector throughput by approximately 15 %
compared to MRC for both single-antenna transmission and GoB. As for the cell-edge throughput, IRC
achieves a 150 % gain w.r.t. MRC for single-antenna transmission. In terms of average SINR, the gain
brought by IRC over MRC is about 1 dB for single-antenna transmission, and 1.5 dB for 4 transmit
antennas with GoB. Nevertheless, the highest SINR improvement is brought by the GoB, which enables a
4.5 dB gain w.r.t. single antenna transmission for the MRC receiver. This SINR gain translates into a 58
% increase in sector throughput when the GoB is used with MRC at the UT compared with single-antenna
transmission. When combined with IRC at the UT, the sector throughput improvement rises to 80 %. The
GoB also provides a huge improvement w.r.t. single-antenna transmission for the cell-edge users, with
gains of 720 % when used with the MRC and 810 % when used in conjunction with IRC at the UT.
The gains brought by the multiple antenna techniques are generally lower in the frequency-adaptive mode
than in the non-adaptive mode, but still remain very attractive. In particular, the combination of GoB and
MRC now yields almost the same gain as single-antenna transmission with MRC: 10 % increase
compared to the single transmit antenna with MRC at the UT. The gain provided by the combination of
GoB with IRC over single-antenna transmission with MRC falls to 20 % (compared to 80 % in the nonadaptive
mode). This behaviour is partly explained by the robustness w.r.t. inter-cell interference
provided by the frequency adaptivity, even in the absence of interference mitigation processing. More
particularly, the relative gains of the GoB in terms of cell-edge throughput are approximately reduced by
a half in the frequency-adaptive mode compared to the non-adaptive mode, whereas the IRC gains (with
single-antenna transmission) remain approximately the same for both modes. The reason is that the
interfering beams, and thus the interference environment the cell-edge users are particularly sensitive to,
change at each frame in an unpredictable manner for the UT. The benefits of frequency adaptivity are
consequently reduced, since this mode relies on the channel quality predictability. For the same reason,
the cell-edge throughput improvement brought by IRC over MRC with GoB transmission is higher in the
frequency-adaptive mode (+20 % vs +11 % in the non-adaptive mode), although is roughly the same in
both modes with single-antenna transmission. By allowing the interfering beams to be efficiently
mitigated when they hit the UT (since the interference is then generally dominated by one or a small
number of interfering sources, which are conditions where the IRC performs well), IRC has the ability to
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smooth the interference variability induced by the GoB, and thus to improve the reliability of the link
adaptation.
C.4 Conclusions
IRC at the receiver and GoB at the transmitter appear as very efficient means to combat inter-cell
interference. Furthermore, both techniques can be combined in order to provide the best performance, and
complement each other very well. The gains brought by the multiple antenna techniques are generally
lower in the frequency-adaptive mode than in the non-adaptive mode, but still remain very attractive.
It should be kept in mind that the practical efficiency of the IRC is expected to depend on the accuracy of
the estimation of the associated combining weights. Further studies should consequently assess the
sensitivity of this estimation to the system performance. In addition, the gains achieved by the GoB partly
originate from the directivity of the interference generated by the neighbouring BSs. However, since the
real-world networks are likely to use a combination of GoB and SDMA in the inner part of the cells, and
GoB without SDMA for cell-edge users, the gains brought by the GoB are expected to be lower in realworld
systems than reported in this study, especially for the cell-edge users. Further studies should
consequently quantify lower bounds on the GoB gain against inter-cell interference, which can be made
by assessing the GoB in the presence of fully non-directive inter-cell interference.
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WINNER II D4.7.3 v1.0
Appendix D. Downlink interference mitigation with multiple
antennas and an SDMA scheme
D.1 Description
The investigations concern basically the same schemes as described in the previous appendices i.e. the
use of fixed GoB at transmitter side and a combining algorithm at receiver side (MRC), but additionally
the expected improvement achieved by using SDMA on top of these techniques will be evaluated.
Furthermore the closed loop diversity technique as a first means to achieve diversity at transmitter side is
considered; this will allow comparison between schemes presenting already diversity rather than with the
single antenna case. Description of these different techniques is given in the following sections.
D.1.1 Closed loop transmit diversity
In an open loop transmit diversity scheme the transmitter has no knowledge about the channel and
therefore no feedback information can be used for adjusting the transmit antenna elements. In opposition
using a closed loop transmit diversity scheme allows the base station to generate the weight vector
adaptively with the help of the constant feedback information from user terminal which is summarised in
Figure D-1 for the case of two transmitting antennas [3GPP25214]:
BS
FBI i- 1
FBI
i
Slot i
Ant
1
Ant
2
S ( )
1
t
S ( )
2
t
Step 6
Weights Decision
in BS
Step 1
...... ......
Channel:
H = ( h 1
h2)
Step 5
Feedback Indicator, one bit per slot
UT
Step 2
Channel Estimation:
H ˆ = ( hˆ
ˆ
1
h 2
)
Step 3
Optimal Weight
Calculation:
φ
max
[ P(
w)
]
Step 4
Phase (back) Rotation
Quantization:
φ
Q
Figure D-1: Closed loop transmit diversity.
The UT uses the signals transmitted both from antenna 1 and antenna 2 to calculate the phase adjustment
to be applied at BS side to maximise the UT receiver power. In each slot i , UT calculates the optimum
phase adjustment φ , for antenna 2, by using the current channel estimation result to achieve the maximal
received power. The optimum phase adjustment φ is then quantised into φ
Q
having four possible values
in π / 2 resolution.
The weight w 2
is then calculated by averaging the received phases over 2 consecutive slots.
Algorithmically, w
2
is calculated as follow,
n
n
1
j
w2 = ∑cos(
φ
i
) + ∑sin(
φi
),
2
2
i=
n−1
i=
n−1
where,
⎧ π π ⎫
φ i
∈ ⎨0
π − ⎬ .
⎩ 2 2 ⎭
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WINNER II D4.7.3 v1.0
For antenna 1, w
1
is constant,
D.1.2
Fixed Grid of Beams (GoB)
w
1
= 1/ 2 .
The scheme of Fixed Grid of Beams used in our investigations is described in this section. It corresponds
to the downlink baseline wide area spatial processing scheme: a single-stream GoB scheme used in a
triple sectored site [WIN2D6137].
For transmission to a certain user the beamforming vector v is chosen from a predefined static set of
complex numbered antenna weights, so-called beams. The number of spatial layers Q is here reduced to
one (no spatial multiplexing, no SDMA). The receive vector of each user and sub-carrier has the
following form,
y
{
= H{ {
v
{
p s
{
+ z
{
where v ∈ { v v ... }
M × 1 M R × M T M × 1 1×
1 1×
1 M × 1
R
T
1 2
v I , and I denotes the number of available beams. H is the MIMO
channel matrix, p the transmit power, s the transmitted symbol and z the vector of interference plus noise.
For the selection of beams the baseline scheme uses the simplest case, choosing each user’s best beam on
a long-term basis over the whole frequency band. This requires a minimal feedback rate. It is suggested to
calculate, for the user terminal, the time-frequency averaged receive power per beam i (in reality e.g.
obtained by combining common pilots with the different beam weights in the user terminal)
P i
=∑∑∑
n k s
h
R
2
n, k , s vi
over all indexes n of receive antennas, k of sub-carriers and s of symbols, with h n,k,s being the n-th row
vector of H (the channel from all transmit antennas to a certain receive antenna n) for a certain symbol
and sub-carrier. To reduce the number of calculations this can be sub-sampled e.g. on a chunk grid. For
each user terminal, the beam index i for the beam with the maximum receive power P is signalled back to
its serving BS. The corresponding weights v i of the user terminals best beam are now used for
transmission if this certain user terminal is scheduled. The baseline system is meant to be used with single
stream transmission, thus at any point in time, only one user terminal is scheduled to use one beam in one
chunk (i.e. no SDMA).
The antenna weights v i are calculated from the main beam direction ϑ i of beam i and from the m-th
transmit antenna element position d m for all M elements, with k = 2π/λ being the wave number, according
to:
1
v [ ( ) ( ) ( ) ] T
i
( ϑ
i
) = vi1
ϑi
vi
2
ϑi
... viM
ϑi
with vim ( ϑ) = exp( − jkd m
sin( ϑi
))
M
A 4 (8)-el. uniform linear array with λ/2 spacing has the element positions:
d
m
1 3 ⎛ 5 7 ⎞
= ± λ , ± λ , ⎜ ± λ , ± λ ⎟
4 4 ⎝ 4 4 ⎠
For a 120° sector with 4 antenna elements, 8 beams are chosen with beam directions according to equal
spacing in beamspace (resulting in equal beam crossing levels in the directivity pattern), which gives the
following beam directions ϑ i in degrees:
[-49.3000 -32.8000 -19 -6.2000 6.2000 19 32.8000 49.3000]
In a 120° sector for the case of 8 antenna elements, 16 beams are chosen with the resulting beam
directions ϑ i in degrees:
[-54.3000 -44.7000 -36.5000 -29.2000 -22.3000 -15.7000 -9.3000 -3.1000
3.1000 9.3000 15.7000 22.3000 29.2000 36.5000 44.7000 54.3000]
The resulting beam patterns are shown in Figure D-2 below.
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WINNER II D4.7.3 v1.0
Figure D-2: 120° sector with 70° HPBW, left: 8 beams for 4 elements, right: 16 beams for 8
elements.
D.1.3
Fixed beam design and scheduling for GoB SDMA
Several enhancements are possible for fixed beams when using SDMA. The shape of the beam directivity
pattern can be altered by tapering, which will be described in detail below. Adaptive scheduling gives
additional performance. The design of the score based scheduler has some options left. One possible
solution is described at the end of this section.
Tapering can be used on top of the fixed GoB to improve the shape of the beam directivity pattern for
SDMA. For the investigations Chebyshev tapering [Dol46] was chosen as it is optimal in the following
sense: For a given side lobe level, the width of the main lobe is minimised. When using SDMA with fixed
beams, increasing tapering decreases cross-talk between side lobes and increases robustness due to beam
mismatch, as seen in Figure D-3. Tapering is not recommended for single stream GoB (non-SDMA) as
lower side lobes are not needed there. The calculation of the tapering vector gives real valued filter
coefficients, denoted as t . As described in the previous section for the WINNER wide area baseline
spatial processing the design of the untapered weights w
baseline
is based on the steering vector and the
beam directions which are equidistant in a cosine space. Tapering for each beam k can now be done on
top of it by element-wise multiplying the amplitudes of the complex weights of baseline GoB by the
tapering vector of the desired approach (with i denoting the index of the antenna element):
w w ⋅ t
tapered , i,
k
=
baseline,
i,
k
Tapering results in unequal transmit power for each antenna element. A key question here is the
efficiency of the power amplifier. A possibility to re-balance the power between the Tx amplifiers is to
shift single or dual antenna traffic (e.g. like non-preamble broadcast channels) to the outer elements of the
array where the shared data channel for tapered SDMA grid of beams uses less Tx power than in the
centre elements. Diversity schemes would benefit from the antenna spacing and the power amplifiers
could be used efficiently.
i
Figure D-3: Pattern for 8 elements, 16 beams with and without side lobe suppression.
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User selection is based on scheduling score and best beam index. The score is calculated as follows: For
the best beam a CQI feedback per chunk is needed. For a given frame, for each chunk in frequency
direction a CQI feedback is available and the ranking of each user’s latest CQI feedback determines its
score. Each chunk now is an independent instance for user allocation. The user with the highest score is
allocated first. When users and thus corresponding beams are allocated, neighbour beams on each side are
blocked. E.g. for WINNER C2 channel model [WIN2D111] with 4 antennas and 8 beams or 8 antennas
and 16 beams, two neighbour beams on each side will not be used in this chunk for scheduling to avoid
causing significant intra-cell interference (the number of blocked beams depends on the scenario, the
SNR and number of beams used). Now the user with the second highest score will be allocated, except its
corresponding beam is already blocked. This approach is continued until all users are checked or a desired
maximum number of spatial streams (e.g. 4) is reached.
D.2 Scenarios
The focus of the studies conducted has been on the Wide Area case where the WINNER II wide area
deployment scenario base urban coverage with channel model C2 [WIN2D111] is considered for the
downlink. The frequency-adaptive case is considered for OFDMA. The closed loop diversity technique is
used with 2 antennas spaced by ten wave lengths. The fixed GoB scheme is used with 2 resp. 4 antennas
building 4 resp. 8 beams where the elements are spaced by half a wavelength in each array. Further
settings are according to the baseline defined in [WIN2D6137].
D.3 Evaluations
The investigations are conducted by means of system level simulations (Monte Carlo snap-shots based
simulator class III [WIN2D6131]). The inner 3-sector site is surrounded by a first tier of 6 neighbouring
sites and the wrap-around technique is used. All links are simulated and all cells are taken into account for
the evaluations. Table D-1 summarises the simulation assumptions:
Deployment scenario
Channel model
Table D-1: Simulation assumptions.
Number of sectors 21
Number of mobiles per sector
UT velocity
UT Receiver diversity
Link adaptation
WINNER II Base Coverage Urban
C2
10 in av.
3 km/h
MRC
Ideal
HARQ / max number of re-transmissions Yes / 4
MCS 27 schemes used: 10 for QPSK, 9 for 16QAM, 8
for 64QAM
Turbo codes
Multiple Access scheme
OFDMA
Scheduler
Score based proportional fair
Traffic model
Full buffer
D.4 Results
The behaviour of the average user and sector throughput for the different techniques are presented in
Figures D-4 to D-6 below, whereby the case closed loop transmit diversity is given here as reference
rather than a technique without any diversity at the transmitter side (BS).
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WINNER II D4.7.3 v1.0
1
cdf of av. user TP - vel. 3 km/h
0.9
0.8
0.7
0.6
cdf
0.5
0.4
0.3
0.2
0.1
Tx div closed loop: 2 x Rx: 2 (MRC)
Tx: fix GoB 2 x Rx: 2 (MRC)
Tx: fix GoB 4 x Rx: 2 (MRC)
Tx: tap. fix GoB 4 x Rx: 2 (MRC) & SDMA
0
0 10 20 30 40 50 60
TP [Mbit/s]
Figure D-4: CDF of the average user throughput.
0.5
cdf of av. user TP - vel. 3 km/h
0.45
0.4
0.35
0.3
cdf
0.25
0.2
0.15
0.1
0.05
Tx div closed loop: 2 x Rx: 2 (MRC)
Tx: fix GoB 2 x Rx: 2 (MRC)
Tx: fix GoB 4 x Rx: 2 (MRC)
Tx: tap.fixGoB 4 x Rx: 2 (MRC)& SDMA
0
0 2 4 6 8 10 12
TP [Mbit/s]
Figure D-5: CDF of the average user throughput (detail).
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WINNER II D4.7.3 v1.0
average sector throughput
140.00
120.00
av sector TP (Mb/s)
100.00
80.00
60.00
40.00
20.00
0.00
Tx: diversity
closed loop 2
Rx: 2 (MRC)
Tx: fix GoB 2
Rx: 2 (MRC)
Tx: fix GoB 4
Rx: 2 (MRC)
Tx: tap.fix GoB 4
Rx: 2 (MRC)
& SDMA
Figure D-6: Average sector throughput.
As expected the throughput values are improved first by using a fixed GoB scheme instead of the closed
loop transmit diversity technique and then by increasing the number of transmit antennas at BS side from
2 to 4. The best performance is reached by the SDMA technique combined with the tapered fixed GoB
built by 4 antennas as described in the previous sections which brings a significant gain of approx. 47 %.
The cell edge user throughput (5% CDF) is increased by using a fixed GoB instead of the closed loop
transmit diversity technique at BS side. However for this assessment criterion the fixed GoB technique
built with 4 antennas performs better than the tapered fixed GoB with the same number of antennas used
together with SDMA.
Table D-2 below illustrates these results, whereby the sector spectral efficiency is also given in order to
complete the picture:
Cell edge user
throughput (Mb/s)
Table D-2: Performance of the investigated schemes.
Tx: div. closed
loop 2
Rx: 2 (MRC)
Tx: fix GoB 2
Rx: 2 (MRC)
Tx: fix GoB 4
Rx: 2 (MRC)
Tx: tap. Fix
GoB 4
Rx: 2 (MRC) &
SDMA
2.64 3.11 3.72 3.35
gain 0% 17.8% 40.9% 26.9%
Average sector
throughput (Mb/s)
83.72 94.5 102.93 122.8
gain 0% 12.9% 22.9% 46.7%
Sector spectral
efficiency (bps/Hz)
1.67 1.89 2.06 2.46
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The lower performance at cell edge of 14 % compared to the fixed GoB obtained by the tapered fixed
GoB together with SDMA can be explained by the fact that this later technique has been optimised so far
considering mostly the interference situation inside the cell since the tapering of adjacent beams brings
better isolation between the scheduled users. Furthermore interference is better spread by simultaneous
transmission in several beams keeping the same overall transmit power at BS side. But this degrades the
situation at cell border. Nevertheless this scheme brings the significant performance on the average sector
throughput of almost 24 % compared to the fixed GoB which corresponds to a spectral efficiency of
almost 2.5 bps//Hz/sector.
Figure D-7 shows the average SINR measured at UT receiver output for the different techniques. Using
for the transmit diversity at BS side a fixed GoB based on 2 antennas leads to an improvement of approx.
2 dB compared to transmitting in closed loop with 2 antennas. The expected additional gain of 3 dB by
increasing the number of transmitting antennas from 2 to 4 at BS side is reflected here. Concerning the
tapered fixed GoB built by 4 antennas used in combination with SDMA the curve appears as set back
compared to the previous one by approx. 6 dB. This is due to the spatial processing of the users as such as
well as the tapering of the beams and the scheduling algorithm serving up to 3 users simultaneously so
that the power available at each UT receiver is reduced compared to the pure fixed GoB scheme.
UT vel. 3 km/h
1
0.9
cdf of av. SINR
0.8
0.7
0.6
0.5
0.4
0.3
0.2
Tx div closed loop: 2 Rx: 2
(MRC)
Tx: fix GoB 2 Rx: 2 (MRC)
Tx: fix GoB 4 Rx: 2 (MRC)
Tx: tap. fix GoB 4 Rx: 2
(MRC) & SDMA
0.1
0
-10 -5 0 5 10 15 20 25 30 35
av. SINR (dB)
Figure D-7: CDF of the average SINR.
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WINNER II D4.7.3 v1.0
Appendix E. Interplay between smart antennas and other
interference mitigation techniques in downlink
The general intention of these investigations is to see which combinations of scheduling algorithms and
DCA are meaningful in the presence of smart antennas (SA) and what gain in throughput and percentage
of satisfied users they provide.
In particular we investigate the impact of SA on interference in downlink in the presence of some
interference averaging techniques, e.g. Random DCA [WIN2D471] or interference avoidance techniques,
such as Minimum Interference DCA [WIN2D472].
Furthermore, we explore the interplay between SA and scheduling. We investigate the gain of SA in
combinations with scheduling algorithms, which take into account channel quality of the users (e.g.
proportional fair scheduling).
E.1 Simulation assumptions and modelling
In the specified multi-cell environment, we use central-cell technique to account for the impact of
surrounding cells and inter-cell interference. All links are accurately modelled; however, the results are
collected only from three innermost cells. The system parameters are based on [WIN2D6137] for the base
coverage urban scenario.
To reduce the overall simulation complexity, we simplify the resource modelling specified in
[WIN2D6137]. One resource unit (RU) represents 3 chunks in the simulator. Therefore, in total 48 RUs
are simulated.
Link adaptation is used to choose an appropriate modulation and coding scheme (MCS) from 10 different
MCS levels specified in Table 3.6 of [WIN2D6137]. We consider four different modulation schemes
(BPSK, QPSK, 16QAM and 64QAM) and 3 possible code rates (1/2, 2/3 and 3/4) of block LDPC codes.
We consider perfect time and frequency synchronisation at system level, and thus any inter-symbol or
inter-carrier interference is neglected.
All possible combinations of DCA (Random DCA, Minimum Interference DCA) and scheduling
algorithms (Round Robin, Proportional Fair) are simulated. DCA and scheduling are executed every 0.69
s and 0.69 ms, respectively. We summarise specific simulation parameters in Table E-1.
Table E-1: Simulation parameters specific for DCA, Scheduling and SA.
Channel model
C2
UT velocity
3 km/h
Multiple access
OFDMA
Central cell
Yes
Interference modelling All links modelled
Number of chunks per RU 3
Traffic model
Realistic FTP model
Scheduler
Round Robin, Proportional Fair
DCA
Distributed Random or
Minimum Interference
Link adaptation
Ideal
Retransmission / HARQ Yes
Number of SA elements 4
Smart antennas are modelled by four-element circular arrays with λ/2-spacing at the base stations. In the
simulations, it was assumed that the DoA statistics follow a Laplacian distribution with angular spread S θ
= 10° = π/18 rad. Instead of generating Laplacian distributed random angles during the run-time of the
simulation, the following approach was adopted for reducing the run-time: For an individual user at a
geometrical angle θ 0 relative to its base station, we define the expected beam pattern conditioned on θ 0 :
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WINNER II D4.7.3 v1.0
B θ,
θ ) = E[
B(
θ −θ
, θ ) | θ ] = A B ( θ −θ
, θ ) e
(
0
0 0 0
0 0 0
−π
where a = √2/S θ and A = [S θ (1 – exp(-π√2/S θ ))] -1 . Thus, the system level simulation was performed with
the expected beampattern (with the main-lobe directed to the user of interest) instead of the random
realisation. The smoothing operation is a circular convolution and evaluated efficiently via the FFT. The
shape of the main-lobe is essentially unaffected by the smoothing, but considerable power leakage into
the nulls of the ideal pattern is observed. To reduce the run-time even further, the smoothed beampattern
was approximated as rotationally invariant: the dependency of B (θ − θ 0 , θ 0 ) on the steering angle θ 0 was
neglected. This is a good approximation for a uniform circular array. Finally, the smoothed beampattern
was segmented into piece-wise constant angular intervals and stored in a ring buffer during the
simulation.
π
∫
−a θ
dθ
E.2 Simulation results
In Figure E-1 we depict the mean system interference in dependence of load (number of users per cell) for
different algorithm combinations.
-93
-94
Mean Interference [dB]
-95
-96
-97
-98
-99
-100
-101
-102
0 10 20 30 40 50 60
Number of users per cell
Min I / Prop Fair
Rand DCA / Prop Fair
Rand DCA / Prop Fair / SA
Rand DCA / Round Robin / SA
Min I / Round Robin
Rand DCA / Round Robin
Min I / Prop Fair / SA
Figure E-1: Mean interference for different algorithm combinations and different loads.
From Figure E-1 we can see that the most interference reduction comes from proportional fair scheduling
(2-5 dB) followed by SA (3 dB), whereas interference reduction from DCA is almost negligible (at most
0.3 dB).
The maximum antenna gain achievable with the array consisting of N basic elements, assuming that
each basic antenna element is affected with the same power, is 10 ⋅ log 10
( N ) . If only a subset of
available channels is used, the interference reduction by SA is decreased accordingly, i.e.:
[dB] SA gain
≈10
⋅ log10
( N )
number of channels used
⋅
total number of channels
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WINNER II D4.7.3 v1.0
Setting in the equation above the number of antennas elements N = 4 with ½ of total channels selected by
DCA, we obtain the gain of SA about 2-3 dB almost independently of load as can be also seen from
Figure E-1.
In Figure E-2 we can see the impact on interference for different methods from the system performance
point of view. We depicted here the dependency of the E2E throughput from the system load for the
considered techniques.
2000
1800
1600
E2EThroughput [kbps]
1400
1200
1000
800
600
400
200
0
0 10 20 30 40 50 60
Number of users per cell
Min I / Prop Fair
Rand DCA / Prop Fair
Min I / Round Robin
Rand DCA / Round Robin
Rand DCA / Prop Fair / SA
Rand DCA / Round Robin / SA
Min I / Prop Fair / SA
Figure E-2: End-to-end throughput for different algorithm combinations and different loads.
As can be seen from Figure E-2, similarly to the case of interference, the most gain comes from
proportional fair scheduler followed by SA and DCA. The relative gain of SA regarding the throughput is
even lower than regarding interference (compare with Figure E-1). The gain of DCA is almost negligible
in the case of throughput as in the case of interference, since a small interference gain can not be
exploited due to coarse granularity of link adaptation and available modulation schemes.
From Figure E-2 it can also be seen that the gains of DCA, scheduler and SA are not additive. For
example, if users with “good” channels are scheduled by proportional fair scheduler, relatively low gain
can be additionally achieved with SA. This is because users already have “good” channels due to
proportional fair scheduling and can achieve almost their maximal data rate, especially for higher loads
where multi-user diversity is high. Furthermore, path-loss including geometrical path-loss, slow- and fastfading
play an important role. Whereas standard deviation of interference was observed in the range of 4-
6 dB, standard deviation of path-loss was in the order of 8-10 dB (due to slow-fading). The channelquality
based scheduler can better exploit this path-loss dynamics than the dynamics in interference.
The similar conclusions can be also extended to SDMA. By SDMA users are effectively “de-coupled”
from each other and interference might play less important role for scheduling decisions than path-loss.
Therefore exploiting path-loss dynamics by efficient scheduling would be of crucial importance for
SDMA systems as well.
Note that there is no single optimal scheduler for all load and QoS requirements i.e. the scheduler should
be able to adapt its parameters according to load and service type, see e.g. Cost Function based scheduler
as described in [WIN2D472].
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WINNER II D4.7.3 v1.0
E.3 Conclusions
The highest interference reduction comes from proportional fair scheduling and SA, whereas the gain
from Minimum Interference DCA is relatively low. Furthermore, only certain combinations of algorithms
are meaningful, since application of one algorithm (SA or proportional fair scheduling) could make the
application of the other one almost superfluous (Minimum interference DCA).
The gains of DCA, scheduler and SA are not additive regarding the user throughput. Again, the highest
throughput gain results from the scheduler that takes into account users’ channel quality.
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WINNER II D4.7.3 v1.0
Appendix F. Complexity reduction of interference mitigation at UT
receiver using multiple antennas
F.1 Description
Multiple antennas can be used at the UT for mitigating the interference experienced. As previously
described in Section 3.3, this is achieved by an appropriate choice of the combining weights used to sum
the signals arriving at the UT’s antennas. In particular, here the MMSE approach for choosing
Interference Rejection Combining (IRC) coefficients will be studied.
Whilst it is well known that IRC can provide performance gains, more insight would be beneficial
concerning the range of situations in which these gains are most worthwhile and are readily exploitable.
In particular, this study addresses computational complexity. The calculation of IRC coefficients involves
multiple matrix inversions, which create a significant computational load. Options to reduce the
computational complexity, benefiting UT battery life, whilst minimising any degradation to performance,
are studied here. Through the use of common pilots, it is assumed that knowledge of the channel of pilotcarrying
sub-carriers is available. Interpolation techniques are used to enable calculation of IRC
coefficients for all other sub-carriers. It is in these interpolation techniques that we aim to reduce
complexity.
Two different interpolation techniques are considered: Channel Interpolation (CHAN INP) and Equaliser
Interpolation (EQUZ INP), which are explained below.
In the downlink, pilots are used by the UT to estimate channel properties. Different types of pilot signals
have already been studied by WINNER and an overview is provided by [WIN1D210]. Either common or
dedicates pilot signals can be used, where common pilots are shared by all the terminals within one
cell/sector, resulting in potentially low pilot overhead per user. On the other hand, dedicated pilots for
each user will improve the quality of channel estimation under some scenarios, compared to common
pilots. Common pilot signals are used as the base point for this investigation. There are two ways to
multiplex common pilots with data, time domain multiplexing (TDM) and frequency domain
multiplexing (FDM). The relative advantages and drawbacks of each technique are summarised in
[WIN1D210]. Since the main aim of this investigation is to design receiver techniques combating the
channel fading at the frequency domain, the frequency expanding technique (FET), one of the FDM
techniques where extra sub-carriers are used to carry pilots, is considered. However, the conclusions in
this report based on one method can be applied to the other method directly.
In an OFDM system, one signal stream is firstly divided into several parallel sub-streams and then
transformed into a data block by an N c point IFFT operation. A cyclic prefix with length N g is inserted at
the head of a block. The OFDM signal reaches the receiver through a multipath channel. Multiple
transmit and receive antennas are considered where n T and n R denote the number of transmit and receive
antennas, respectively. Assuming the use of the cyclic prefix is capable of preserving the orthogonality
between different sub-carriers and completely eliminating the ISI between consecutive OFDM symbols
simultaneously. After removing the cyclic prefix followed by the FFT operation at the receiver side, the
received signal for the sub-carrier K at antenna n, providing the bandwidth of the interference and the
desired signal is completely overlapping, is:
Y
( n)
nT
∑
( K)
= X ( K)
H ( K)
+ X ( K)
H ( K)
+ W ( K)
(1)
i=
1
( i)
( i,
n)
where X (i) (K) represents the transmitted signal at sub-carrier K from transmit antenna i and H (i,n) (K)
denotes the frequency response of the channel between antenna pair i and n for the K-th sub-carrier.
X I (i) (K) represents the signal transmitted from both the other sectors within one base station and other base
stations, i.e., interference signal, where H I (i,n) (K) represents the channel of the transmit-receive antenna
pair (i,n). W (n) (K) is the Fourier transform of the additive white Gaussian noise (AWGN) with power σ n 2 .
Q denotes the total number of the interference sources and it is the product between the number of
interference base stations, the number of sectors within each base station, and the number of transmit
antennas at each sector. The transmission channel is a frequency selective channel which means:
H
( i,
n)
Q
∑
i=
1
= ∑
( i , n)
−
( K)
h e
r
r
( i)
I
( i,
n)
τ r K
j2π
TNc
( i,
n)
I
( n)
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WINNER II D4.7.3 v1.0
( i,
n)
where h (i,n) r and τ r are the gain and delay of the rth path of antenna pair (i,n), respectively. T represents
the period of an OFDM symbol.
It has been proved that for FET, the best design of the pilot signal is to multiplex pilots with data
periodically [DT02], i.e., the distance between any two consecutive pilots in frequency domain is
identical. Here we consider an OFDM system with N p th pilots where N p = N c /S, i.e., one pilot symbol is
followed by S-1 data symbols and S is the distance between two consecutive pilots. For a base station
with multiple transmit antennas, in order to block the cross-talk between pilots from different transmit
antennas at one particular receive antenna, the pilot carrying sub-carriers used by one transmit antenna
may not be used by any other transmit antennas, i.e., if sub-carrier i is used by transmit antenna k to send
pilots, then “NIL” will be transmitted at sub-carrier i for the transmit antenna j (j = 1… n T , j≠k). From
(1), a subset of the received signal at receive antenna n which only consists the pilot symbol transmitted
from one particular transmit antenna (assuming the first transmit antenna), if expressed in vector-matrix
format, can be defined as:
( n,1) =
(1)
Q
(1, n) ( i) ( i, n) + ∑ I I
+
( n,1)
i= 1
y X H X H W (2)
where y (n,1) = [Y (n) (0S+1), Y (n) (S+1),…, Y (n) (N p S+1)] T represents the received signal from transmit antenna
1 at the sub-carriers for pilot signal transmission, x (1) = diag[X (1) (0S+1), X (1) (S+1),…, X (1) (N p S+1)] is the
pilots, H (1,n) = [H (1,n) (0S+1), H (1,n) (S+1),…, H (1,n) (N p S+1)] T represents the channel. X I
(i)
= diag[X I (i) (0S+1),
X I (i) (S+1),…, X I (i) (N p S+1)] and H I
(i,n)
= [H I (i,n) (0S+1), H I (i,n) (S+1),…, H I (i,n) (N p S+1)] T denote the transmitted
symbol from interference source i and the channel between interference source i and receive antenna n.
W (n,1) = [W (n,1) (0S+1), W (n,1) (S+1),…, W (n,1) ( N p S+1)] T represents the Fourier transform of the white
Gaussian noise of the antenna pair (n,1).
In practice, the index of sub-carriers used by pilots of transmit antennas from 2 to n T is the shift of the
index of antenna 1 by a pre-defined value [3GPP36211], for example, the received signal at antenna n
which only consists the pilot symbol transmitted from antenna two is:
( n,2) =
(2)
Q
(2, n) ( i) ( i, n) + ∑ I I
+
( n,2)
i= 1
y X H X H W
where y (n,2) = [Y (n) (0S+2), Y (n) (S+2),…, Y (n) ( N p S+2)] T represents the received pilots from transmit antenna
2 where the index of pilot-carrying sub-carriers is shifted by 1. Similarly, x (2) = diag[X (2) (0S+2),
X (2) (S+2),…, X (2) (N p S+2)], H (2,n) = [H (2,n) (0S+2), H (2,n) (S+2),…, H (2,n) (N p S+2)] T and W (n,2) = [W (n,2) (0S+2),
W (n,2) (S+2),…, W (n,2) ( N p S+2)] T .
In order to calculate the combining weights of the receive antenna, the channel coefficients between any
transmit and receive antenna pair (i,n) at every sub-carrier need to be known by terminals. This
information can be attained by firstly estimating channel coefficients at the pilot-carrying sub-carriers and
then calculating channel coefficients of the data-carrying sub-carriers by interpolating. Based on (2), the
estimation of the channel coefficients of the pilot-carrying sub-carriers is a traditional estimation problem
with plenty of well-known solutions. Among them, one widely used method is the minimum variance
unbiased estimation (least square) and the estimator is:
( n,1)
(1) H −1 (1) −1 (1) H −1
θ = [( X ) C X )] ( X ) C (3)
LS
H
⎛
Q
⎞ ⎛
Q
⎞
where ⎜ ( i)
( i,
n)
( n,1)
= + ⎟ ⎜ ( i)
( i,
n)
( n,1)
C + ⎟
⎜∑
XI
H I W
⎟ ⎜∑
XI
H I W is the autocorrelation matrix of the
⎟
⎝ i=
1
⎠ ⎝ i=
1
⎠
interference plus noise. If the interference is not exploited but treated as white Gaussian noise, (3) can be
simplified as:
( n,1)
=
)
(1) −1
θLS
X (4)
i.e., the channel coefficients for (iS+1)th sub-carrier can be obtained by simply dividing Y (n) (iS+1) by
X (1) (iS+1).
To reduce potential noise increment caused by the LS method, minimum mean square error (MMSE) can
be used to design the channel estimator and it is:
Q
( n,1)
(1, n) 2 (1, n) 2 ( i, n) 2 −1 (1)
MMSE
= ( σ (1) + σ ( i ) I
+ σn
)
X ∑ XI
i=
1
θ R R R I X (5)
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WINNER II D4.7.3 v1.0
σ X
2
2
where σ X
(1) and ( i )
source i, respectively. R (1, n)
and
I
denotes the power of the desired pilot signal and interference pilot signal from
R represent the autocorrelation matrix of H (1,n) and H I
(i,n)
(, in )
I
respectively. If the interference is treated as white Gaussian noise, (5) can be simplified as:
( n,1)
MMSE
Q
(1, n) 2 (1, n) 2 2 −1 (1)
( σ (1) σ ( i ) σn
)
X ∑ XI
i=
1
θ = R R + I + I X (6)
Comparing with LS method, the MMSE solution requires extra information, the second order statistical
property of the channel as well as significantly computational complexity increase, mainly caused by
matrix inversion operation. If all available pilots are used to calculate the
θ
( n,1)
MMSE
, the dimension of the
matrix is quite large, especially for a wideband system with long delay spread which will reduce the
bound of S. Under this scenario, window techniques can be used which means the whole bandwidth can
be divided into several small frequency grids, only pilots within a frequency grid are used to calculate the
channel coefficients for this frequency grid [AK05]. The complexity can be further reduced by using a
reduced rank autocorrelation matrix through singular value decomposition [OSB98].
For each antenna pair (i,j), an estimator needs to be calculated for the desired pilots and the total number
of estimators is n T n R irrespective of which method is used.
For the channel estimation based on FET, an efficient interpolation technique is necessary in order to
generate channel coefficients at data carrying sub-carriers by using the channel information at pilot
carrying sub-carriers. In the linear interpolation algorithm, two successive pilot sub-carriers are used to
determine the channel coefficients for data carrying sub-carriers that are located in between the pilots.
Considering the data carrying sub-carrier K (K = ⎣K/S⎦ + l and l = 0,1,…S-1) between transmit and
receive antenna pair (i,n), the estimated channel response using linear interpolation method is given by:
H
( i,
n)
( i,
n)
( , )
i n
⎣K
/ S⎦)
+ l / S × H ( ⎣K
/ ⎦ + 1)
( K)
= (1 − l / S)
H (
S
The interpolation quality can be improved by using extra pilots with suitable weighting factors. For
example, the linear interpolation can utilise the whole N P pilots to calculate the channel coefficient of
each data carrying sub-carrier. Alternatively, higher-order polynomial non-linear interpolation, with the
computational complexity and the number of pilots being used growing with the order, will fit the channel
better than the linear interpolation. In fact, the distance between consecutive pilots, S and the order of the
polynomial interpolation, consist of a pair of parameters which provide the tradeoff between the quality
of the channel estimation and the computational complexity. As S increases, the computational
complexity of the channel estimation for the pilot carrying sub-carriers is reduced, which means more
computational power can be used to improve the interpolation quality through high order polynomial
interpolation. For a system with small S, the benefit by using higher-order polynomial interpolation is
limited hence it is desirable to use linear interpolation to reduce the computational complexity. The value
of S is strongly influenced by the delay spread of the channel. Since WINNER aims to provide ubiquitous
coverage for significantly different environments, intuitively, the design of S should satisfy the
requirement of the worst case which means its value maybe quite small for scenarios with small delay
spread, for example, indoor environment. Hence, in this study, the linear interpolation is the main
interpolation technique to be considered.
With the knowledge of the channel coefficients for every sub-carrier, the combining coefficients of the
receive antenna array for sub-carrier K can be determined based on particular criteria. At here, the MMSE
method is considered. The solution for sub-carrier K is:
Q
H i i
MMSE
= σX
+ ∑σ ( i ) I I
+ σ
X
n
I
i
w H% H% H% H% I H% (8)
2 2 2 −1
( ( K) ( K) ( K) ( K) ) ( K)
= 1
2
where σ X
denotes the power of the data of any transmit and receive antenna pair (i,j) with the assumption
that the transmission power is equally allocated between different transmit antennas. The definition of
H % H ( K)
is:
(1,1)
(1, n )
⎡
T
h% ( K) L h%
( K)
⎤
H ⎢
⎥
H% ( K)
= ⎢ M O M ⎥
⎢ ( nR,1) ( nR, nT
)
h% ( K) h ( K)
⎥
⎣ L %
⎦
(7)
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WINNER II D4.7.3 v1.0
which represents the estimated channel matrix for sub-carrier K.
T
i
(,1) i
(, inR
)
H %
I( K) = ⎡h% I
( K) h%
I
( K)
⎤
⎣
L ⎦
denotes the estimated channel vector between interference
source i and the receive antennas.
Solving (8) requires the channel state information of each interferer, which requires n R Q channel
estimator and corresponding interpolation operations. To simplify the processing at the receiver side, a
sub-optimal solution treating the interference as white Gaussian noise results in the following solution
providing the terminal knows the large scale path loss of the interference link:
Q
2 H
2 2 −1
X ∑ ( i )
X n
I
i 1
w = ( σ H% ( K) H% ( K) + σ I+
σ I) H% ( K)
(9)
MMSE
However, using (9) directly may cause large performance degradation compared with (8). Theoretically,
with the knowledge of the channel of all the interference, the adverse effect caused by the interference
can be further suppressed through the combination of an interference suppressor and an equaliser;
however the computational complexity to address all possible interference is unrealistic. In practice, at
least for base stations following hexagonal layout, most of the interference contribution are dominated by
few interference links. If the channel information of these significant interference links is exploited,
noticeable gain can be achieved with reasonable computational complexity increase, i.e., a trade-off
between (8) and (9). It will also reduce the computational complexity of the following interference
cancellation operation, it they exist. The modified solution is:
Qs
2 H 2 i i
2 −1
MMSE
= σX
+ ∑σ ( i ) I I
+ σ
X
n
I
i 1
w ( H% ( K) H% ( K) H% ( K) H% ( K) I) H% ( K)
(10)
=
where Q s denotes the number of significant interference links and the residual interference links are
completely neglected by the terminal. Hence, identification of the relevant number of significant
interfering links is necessary in order to implement this solution.
The intention of (10) is to reduce the computational complexity during the calculation of the combining
coefficients. The reduction comes from avoiding the channel estimation and interpolation of some weak
interference links. However, (10) needs to be calculated for each data-carrying sub-carrier resulting in
significant computational consumption. A new method that can significantly reduce the computational
complexity is that only the combining coefficients of the pilot carrying sub-carriers are calculated and
these coefficients for the data carrying sub-carriers are obtained by interpolation. However, using this
method requires stricter limitation on the value of S, i.e., the space between two consecutive pilots should
be small enough, since there exist scenarios where little variation in channel causes significantly variation
of the value of the receiver’s combining coefficients. The linear interpolation is also considered at here
and for any data carrying sub-carrier K (K = ⎣K/S⎦ + l and l = 0,1, …S-1), the coefficients obtained by
using linear interpolation method are:
wMMSE ( K) = (1 − l/ S) wMMSE ( ⎢⎣K/ S⎥⎦) + l/ S× w
MMSE
( ⎢⎣K/ S⎥⎦+
1) (11)
The two interpolation techniques considered here are summarised as:
• Channel Interpolation (CHAN INP). In this case, the channel coefficients for the data-carrying
sub-carriers are obtained from the channel coefficients of the pilot-carrying sub-carriers by use
of linear interpolation. Then the IRC coefficients are calculated for each sub-carrier, by matrix
inversion (10).
• Equaliser Interpolation (EQUZ INP). In this case, first the IRC coefficients for the pilot-carrying
sub-carriers are calculated, then the IRC coefficients for the data-carrying sub-carriers are
obtained by linear interpolation of the IRC coefficients for the pilot-carrying sub-carriers (11).
Matrix inversions are only required for the pilot-carrying sub-carriers.
=
F.2 Scenarios
Interference rejection combining techniques at the UT are applicable in all scenarios. Here the evaluations
will be conducted using the wide area base coverage urban parameters and assumptions [WIN2D6137].
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F.3 Requirements
This study requires knowledge of the channel of pilot-carrying sub-carriers, which is assumed to be
available. In the realistic case that this knowledge is known imperfectly, the imperfection is independent
of the IRC coefficient estimation technique, therefore the conclusions of this study remain valid, although
the absolute performance values may be degraded.
F.4 Evaluations
The evaluations are performed as computer simulations of a chunk based OFDMA/TDMA system. The
studies consider a single UT receiving wanted and interference signals from at least 19 BS sites, each
with three sectors (cells) per site. For “CHAN INP” and “EQUZ INP” cases, only the important
interference links whose cumulative power is eighty percent of the total interference power are considered
where the interference power at here is calculated based on the large scale fading. This equates to 7, 18
and 26 interfering links for cell centre, sector border and cell edge, respectively, which provide
representative performance, as discussed in Appendix A.
F.4.1 Assumptions
Beyond the already stated assumptions (base coverage urban, channel estimates for pilot-carrying subcarriers,
and simplified interference modelling), the value of pilot distance S is set to 4.
F.4.2 Results
Figure F-1 shows the SINR performance for these three cases by using different receiver techniques
where “CHAN INP”, “EQUZ INP” and “IDEAL” denote the solution based on channel interpolation,
combining coefficient interpolation and equation (8), respectively. As aforementioned, the curve with
label “ideal” means the channel is fully known. (8) is repeatedly used to calculate the combining
coefficients at each sub-carrier. The “ideal” case takes into account all of the possible interfering links,
not a reduced subset of the most significant as in the other cases.
1
0.9
0.8
0.7
IDEAL
CHAN INP
EQUZ INP
Cell Edge
SINR performance for different receiver techniques
0.6
CDF (%)
0.5
Sector Border
Cell Centre
0.4
0.3
0.2
0.1
0
-20 -10 0 10 20 30 40
SINR per chunk (dB)
Figure F-1: SINR performance for different receiver techniques.
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WINNER II D4.7.3 v1.0
From Figure F-1, it can be seen that the SINR performance of “CHAN INP” and “EQUZ INP” match
each other tightly. However, in the computational complexity point of view, for the “EQUZ INP” method,
firstly, the computational effort used by the interpolation process to obtain the channel coefficients for the
data-carrying sub-carriers can be saved. Secondly, the matrix inversion in (10) for the data-carrying subcarriers
when calculating combining coefficients can also be avoided. Since the computational complexity
of the interpolation operation is identical for these two methods, at least (N-N P )th matrix inversion
complexity is reduced by using “EQUZ INP”, hence this is the recommended technique. The performance
degradation between these two methods and the ideal cases is caused by using channel interpolation or
combining coefficient interpolation for the data-carrying sub-carriers and neglecting unimportant
interference links.
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Appendix G. Uplink interference mitigation with multiple antennas
G.1 Description
This study will investigate and assess the use of multiple antennas at the BS for uplink inter-cell
interference mitigation in the spatial domain. The multiple receive antennas are used to establish diversity
branches so that combining schemes in the baseband signal processing in the BS can be implemented.
The studied combining schemes are traditional MRC as well as IRC.
G.2 Scenarios
Both MRC and IRC are baseband receive processing schemes, and is therefore independent of scenario.
G.3 Requirements
The interference rejection gain provided by IRC depend on that accurate channel and interference
estimates are available at the receiver and it is hence essential to consider such aspects in the pilot pattern
design. It is also beneficial with a time synchronised system in order to maximise the IRC gain, however,
it is not a requirement.
G.4 Evaluations
The evaluations are performed as computer simulations of a non-frequency adaptive OFDMA/TDMA
network, i.e. system level simulations. The studied deployment comprises 19 sites, each with three sectors
(cells) per site. This means that the base coverage urban scenario, defined in [WIN2D6137] as a WA
scenario, is part of the study.
G.4.1 Assumptions
The simulation assumptions follows to a large extent the deployment scenario and system parameters as
specified for the base coverage urban scenario, defined in [WIN2D6137] as a WA scenario. The studied
deployment comprises 19 sites, each with three sectors (cells) per site. Each sector is equipped with an
antenna array comprising two, four or eight elements separated half a wavelength, and MRC or IRC is
implemented for the antenna combining. To handle the single-carrier modulation, MMSE frequency
domain equalisation (FDE) is also implemented in the BS receiver. As a reference case, a system with
single receive antennas at the BSs is also simulated. The user terminals employ single antenna
transmission in all cases.
Two types of scheduling are used: The first one is round robin TDMA, i.e., in each frame a single user
per sector is assigned to the entire transmission bandwidth of 40 MHz. Due to the limited available
transmission power of the UTs, this is expected to be suboptimal. Therefore also a round robin
TDMA/FDMA strategy is simulated where 8 users are frequency multiplexed, each using approximately
5 MHz bandwidth.
The simulations assume perfect channel and interference estimation at the receiver. Overhead such as
pilots, e.g. for channel and interference estimation, or protocol headers are not accounted for. A summary
of some important simulation parameters are given in Table G-1 below.
The used performance measures are post-receiver SINR, the active radio link data rate, and the average
sector throughput.
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Table G-1: Simulation assumptions.
Channel model
C2
Number of base stations 57
Number of users per cell 12 in average
Site-to-site distance 1000 m
Wrap-around
Yes
Interference modelling All links modelled
BS antenna configuration 2, 4 and 8-element ULA, antenna element separation 0.5λ
BS antenna gain 14 dBi
UT antenna configuration Single transmit antenna
UT antenna gain 0 dBi
UT velocity
50 km/h
Coding
Rate 1/3 turbo code with rate matching for rates
1/10, 1/3, 1/2, 2/3, 3/4, 8/9
Modulation
QPSK, 16QAM, 64QAM
Link adaptation
Ideal
Retransmission / HARQ No
Multiple access
B-IFDMA
Scheduling
Round robin TDMA/FDMA
G.4.2 Results
Figure G-1 shows CDFs of the average post receiver SINR. The left panel (a) is for TDMA round robin
scheduling where each user gets the entire transmission bandwidth of 40 MHz, and the right panel (b) is
for TDMA/FDMA round robin scheduling where eight users are frequency multiplexed. For both cases
we show results for three configurations; one receive antenna and four receive antennas with MRC and
IRC, respectively. For TDMA scheduling it can be seen that employing four receive antennas improves
the average post receiver SINR by 5.5-6 dB for all users, and that there is almost no difference between
MRC and IRC. Also for TDMA/FDMA scheduling, the gain of going from one receive antenna to four
receive antennas with MRC is about 5.5-6 dB, but in this case IRC gives an additional gain of
approximately 1 dB. The reason for this is that in this case the interference level per sub-carrier is higher
since the UTs transmission bandwidth is 5 MHz compared to 40 MHz. A higher interference level in the
network means that IRC should be more efficient. This is supported by Figure G-2 which shows the
average sub-carrier interference. If we look at the 50-percentile we can see that the interference level is
about 4 dB higher in the network with TDMA/FDMA scheduling.
If we go back to Figure G-1, it can be further seen for the single receive antenna case that the 50-
percentile of SINR is -5 dB. If we look at the same measure but now for TDMA/FDMA scheduling, it is
about -1 dB. Again, the explanation is the higher transmission power per sub-carrier in the TDMA/FDMA
scheduling case.
100
100
90
90
80
80
70
70
C.D.F. [%]
60
50
40
C.D.F. [%]
60
50
40
30
30
20
M RX
= 1
20
M RX
= 1
10
M RX
= 4: MRC
M RX
= 4: IRC
10
M RX
= 4: MRC
M RX
= 4: IRC
0
-20 -15 -10 -5 0 5 10 15 20
SINR [dB]
0
-20 -15 -10 -5 0 5 10 15 20
SINR [dB]
(a) TDMA scheduling
(b) TDMA/FDMA scheduling
Figure G-1: CDFs of average post receiver SINR.
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WINNER II D4.7.3 v1.0
100
90
80
70
C.D.F. [%]
60
50
40
30
20
10
TDMA
TDMA/FDMA-8
0
-125 -120 -115 -110 -105
average sub-carrier interference [dBm]
Figure G-2: CDFs of average sub-carrier interference.
In Figure G-3 the CDFs of active radio link rate, i.e. user data rate when transmitting, are shown. As for
post receiver SINR above, the left panel (a) shows results for TDMA scheduling, and the right panel (b)
shows results for TDMA/FDMA scheduling. Here it can clearly be seen that the TDMA scheduling
approach is unfeasible, since it means that about 25 % of the users will not get any throughput at all in the
SISO case. The fairness is better in the TDMA/FDMA scheduling case, but the achievable active radio
link rate is of course lower due to smaller transmission bandwidth. One interesting observation is that the
gain of IRC compared to MRC is rather limited. The reason for this can be discussed, but two theories are
most probable:
• The antenna separation of half a wavelength. With larger antenna separation, e.g. in the order of
10 wavelengths, the IRC gain should probably be larger. This will be further investigated below.
• The colour of the interference. We have seen in Figure G-2 above that the system is interference
limited, but if the interference consists of several interferers of similar strength, it means that the
colour goes towards white, meaning that the performance of IRC goes towards that of MRC. In
uplink we can expect more interferers than in the downlink, which would result in lower IRC
gain.
100
100
90
90
80
80
70
70
C.D.F. [%]
60
50
40
C.D.F. [%]
60
50
40
30
30
20
M RX
= 1
20
M RX
= 1
10
M RX
= 4: MRC
M RX
= 4: IRC
10
M RX
= 4: MRC
M RX
= 4: IRC
0
0 20 40 60 80 100 120 140 160 180 200
User data rate when transmitting [Mbps]
0
0 5 10 15 20 25 30 35 40
User data rate when transmitting [Mbps]
(a) TDMA scheduling
(b) TDMA/FDMA scheduling
Figure G-3: CDFs of active radio link rate (user data rate when transmitting).
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In Figure G-4 the 10 th percentile of active radio link rate is summarised, but here also with results for two
and eight receive antennas added. Here it can clearly be seen that with pure TDMA scheduling we need
four receive antennas in order to get any throughput at all. The reason for this is that we have no retransmissions
(HARQ). If we concentrate on the more realistic TDMA/FDMA scheduling, we can see
that the gain of going to more antennas is quite large. Going from SISO to two receive antennas and MRC
increases the 10 th percentile of active radio link rate from 0.3 Mbps to 0.9 Mbps, i.e. 200 %.
If we look at the cases with four antennas and eight antennas in Figure G-4, it seems as the TDMA
scheduling is better than the TDMA/FDMA scheduling. However, it should then be kept in mind that this
is the user data rate when transmitting, and in the system with TDMA scheduling the users are scheduled
more seldom. Hence, a more correct conclusion can be drawn from Figure G-5 below, which summarises
the 10 th percentile of average user data rate, i.e. computed not only over the time when the user is
scheduled, but also during the time when the user is not scheduled. Here it can clearly be seen that the
TDMA/FDMA scheduling is a better approach than the TDMA scheduling, also for the four antenna and
eight antenna cases.
Figure G-6 below shows the average sector throughput, i.e. the impact on system capacity. Here we can
again clearly see the gain of TDMA/FDMA scheduling compared to the pure TDMA scheduling. Even
though we have seen lower user data link rates in Figure G-4, the frequency multiplexing resulting in
higher power per sub-carrier (due to the limited transmission power of the UTs), is beneficial and sums
up to higher system throughput. With SISO and TDMA/FDMA scheduling the average sector throughput
is slightly above 30 Mbps. By going to two receive antennas and MRC this is increased to around 45
Mbps, i.e. an increase of almost 50 %, and by going to four antennas with MRC the throughput reaches
about 62 Mbps which is an increase of 100 % compared to SISO. With eight antennas and MRC the
throughput reaches 86 Mbps, which is almost three times the SISO throughput. And also here, as can be
expected from the results on active radio link rate above, we can see that the gain of IRC compared to
MRC is rather limited. With two antennas the throughput gain of IRC is only in the order of 2 Mbps (5
%) while for four antennas it is around 6 Mbps (10 %) and for eight antennas 10 Mbps (11 %).
10,0
10th perc. user data rate (when transmitting) [Mbps]
9,0
8,0
7,0
6,0
5,0
4,0
3,0
2,0
1,0
0,0
SISO MRC(2) IRC(2) MRC(4) IRC(4) MRC(8) IRC(8)
TDMA
TDMA/FDMA-8
Figure G-4: 10 th percentile of the active radio link rate (user data rate when transmitting).
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WINNER II D4.7.3 v1.0
300,0
10th perc. average user data rate [kbps]
250,0
200,0
150,0
100,0
50,0
TDMA
TDMA/FDMA-8
0,0
SISO MRC(2) IRC(2) MRC(4) IRC(4) MRC(8) IRC(8)
Figure G-5: 10 th percentile of average user data rate.
120,0
average sector throughput [Mbps/cell]
100,0
80,0
60,0
40,0
20,0
TDMA
TDMA/FDMA-8
0,0
SISO MRC(2) IRC(2) MRC(4) IRC(4) MRC(8) IRC(8)
Figure G-6: Average sector throughput.
As has been pointed out above, the relative gain of IRC compared to MRC has been rather limited. One of
the reasons can be the small antenna separation of half a wavelength. To investigate this, also simulations
with an antenna separation of 10 wavelengths have been performed. The results are summarised in Figure
G-7 which shows the average sector throughput for SISO and four antennas with MRC and IRC,
respectively. If we compare with Figure G-6 above, which shows the average sector throughput for the
case with antenna separation of half a wavelength, it can be seen that the throughput is increased with
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larger antenna separation. With four antennas and MRC the average sector throughput is increased from
slightly above 60 Mbps up to almost 80 Mbps. The same is true for IRC, the average sector throughput
increases from slightly below 70 Mbps up to almost 90 Mbps. However, the relative gain of IRC over
MRC is still rather small. It is larger than in the case with antenna separation of half a wavelength, but
still rather limited. Hence, the reason for the rather small relative gain of IRC over MRC is most probably
the colour of the interference, i.e. that the interference consists of several interferers of similar strength.
To summarise, we have seen that employing multiple receive antennas at the BSs is an efficient means to
combat uplink inter-cell interference. By going from a single receive antenna to two, four, and eight
receive antennas with MRC increases the average sector throughput from around 30 Mbps to 45 Mbps,
more than 60 Mbps, and about 85 Mbps, respectively. The additional gain of IRC is rather limited, which
most probably is due to that the interference consists of several interferers of similar strength meaning
that the colour of the interference is going towards white. This has, however, not been verified. Also the
coverage, here measured as the 10 th percentile of active radio link rate, is increased by having multiple
antennas at the BS. By going from a single receive antenna to two antennas and MRC gives an increase of
200 %, and by going to four and eight antennas this is even further increased.
All this is valid for TDMA/FDMA scheduling with eight users frequency multiplexed each occupying 5
MHz. Results were also shown for pure TDMA scheduling where each user get the entire transmission
bandwidth of 40 MHz, but this is deemed to be an unpractical strategy due to the limited transmission
power of the UTs.
It has also been shown that having an antenna separation of 10 wavelengths instead of half a wavelength,
significantly increases the performance. However, this antenna setup is not in line with the GoB setup that
is proposed for downlink transmissions.
100
90
average sector throughput [Mbps]
80
70
60
50
40
30
20
10
throughput TDMA
throughput TDMA/FDMA-8
0
SISO MRC(4) IRC(4)
Figure G-7: Average sector throughput for the case with antenna separation of 10 wavelengths.
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WINNER II D4.7.3 v1.0
Appendix H. Macro diversity in the form of Cellular Cyclic Delay
Diversity (C-CDD)
H.1 Description
Transmit diversity techniques are applied to a cellular downlink in this study. In single-cell scenarios with
multiple-antenna concepts at the transmitter, transmit diversity can be applied for increasing the
performance at the receiver side. These transmit diversity techniques can be based on, e.g., space time
block codes (STBCs) or antenna diversity schemes [DK01]. These multi-antenna techniques can be
shifted to a cellular scenario by using the neighbouring base stations as the multi-antenna setting.
Therefore, transmit diversity is transformed into macro diversity. In 2002, Inoue et al. [IFN02] proposed
this within the application of STBCs. In contrast to STBCs, the application of cyclic delay diversity
(CDD) to a cellular environment, namely cellular CDD (C-CDD) [PD06] offers the exploitation of the
increased transmit diversity at the receiver without any change on the receiver side. Transmitting the
same signal from several base stations including cyclic delays will be observed as a channel with higher
frequency selectivity at the receiver. This resulting additional frequency diversity can be collected by a
channel code for instance. There exists no rate loss for higher number of transmit antennas/base stations,
and there are no requirements regarding constant channel properties over several sub-carriers or symbols
and transmit antenna/base station numbers. The principle structure of C-CDD is presented in the block
diagram of Figure H-1 without considering the random choice of cyclic delays and power adaptation
blocks.
Figure H-1: Principle of cellular cyclic delay diversity.
At the cell border of a conventional OFDMA system, inter-cell interference exists, and therefore, the used
sub-carrier resources are underachieved. On the other hand, the avoidance of interference does not allow
double allocation of sub-carriers from adjacent BSs. This decreases the exploitation of the sub-carrier
resources per cell site. C-CDD takes advantage of the aforementioned resulting available resources. The
main goal is to increase performance by avoiding interference and increasing diversity at the most critical
environment directly at the cell border.
Radio resource management (RRM) works perfectly if all information about the mobile users, like the
channel state information, is available at the base station. This is especially true if the RRM could be
intelligently managed by a single genie manager. The described C-CDD technique offers an improved
performance especially at the critical cell border without the need of any information about the channel
state information on the transmitter side. Additionally, the inter-cell interference is reduced by lowering
the transmit power within the sub-carriers which are assigned to C-CDD.
H.2 Scenarios
The C-CDD technique can be applied in any cellular scenario in which the cells are overlapping, and
therefore, C-CDD is independent of the scenarios. In this study, the simulations are performed on the link
level for WA.
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H.3 Requirements
Since the BSs have to be coordinated to transmit simultaneously the desired signals including the cyclic
delays, inter-BS communication is required. The transmission from the BSs must ensure that the
reception of both signals is within the guard interval, and therefore, the BSs have to be almost
synchronised. Furthermore, the same sub-carrier resources at each BS have to available for C-CDD.
There are no requirement at the UT to exploit the increased transmit diversity. In contrast to the basic C-
CDD principle the adaptive C-CDD method requires a feedback of the information for the required power
adaptation. The randomly chosen delay does not need any additional configuration at the receiver or the
system concept.
H.4 Evaluations
The evaluations are performed as computer simulations of non-frequency adaptive OFDMA/TDMA on
the link level. Exemplarily, the studied deployment comprises 2 sites, each with one cell per site. The
base coverage urban scenario, defined in [WIN2D6137] as a WA scenario, is taken for the study.
H.4.1 Assumptions
The base coverage urban scenario, defined in [WIN2D6137] with the channel model WINNER C2 for BS
to outdoor UT [WIN2D111] is taken for study. The basic setup of the baseline system configuration is
assumed in the simulations and the given convolutional code of Section 3.2.5 in [WIN2D6137] is chosen.
cyc
For the cyclic delay we assume δ
1
is set to 30 samples.
H.4.2 Results
In the following simulation results for the applied C-CDD in a two-site scenario will be given. Figure H-2
represents the bit error rate (BER) versus the carrier to interference (C/I) ratio. For C-CDD the term C/I is
misleading, as the transmitted signal from the interfering BS is no I (interference). On the other hand it
describes the ratio of the power from the desired base station to the temporary BS and indicates where the
UT is in respect to the BSs. For negative C/I values in dB the UT is closer to the interfering BS and for
positive C/I values is the UT nearby the desired BS. The cell border is defined for C/I = 0 dB.
Figure H-2: BER versus C/I for SNR = 5 dB using C-CDD with full power and halved power per
sub-carrier and no transmit diversity technique.
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The performances of the applied C-CDD method are compared with the OFDMA reference system using
no transmit diversity technique and with a random independently chosen sub-carrier allocation in each
cell site. The reference system is half (RL=0.5) and fully loaded (RL=1.0). We observe a large
performance gain in the close-by area of the cell border (C/I = -10dB…10dB) for the new proposed
diversity technique C-CDD. Furthermore, C-CDD enables an additional substantial performances gain
cyc
compared to pure macro diversity by transmitting the identical signals from both cell sites ( δ 1
= 0 ) at
cyc
the cell border. For δ 1
= 0 no transmit diversity is available at the cell border. The same effect can be
cyc
seen for C-CDD at C/I= -4.6 dB because the artificial delay δ 1
= 30 and the inherent geographical
delay cancel out each other. Since both BSs in C-CDD transmit the signal with the same power as the
single BS in the reference system, the received signal power at the UT is doubled. For higher C/I ratios,
i.e., in the inner cell, the C-CDD transmit technique lacks the diversity from the other BS, and therefore,
the performance merges to the reference performances. To establish a more detailed understanding we
analyse the C-CDD with halved transmit power. For this scenario, the total designated received power at
the UT is equal to the conventional OFDMA system. There is still a performance gain due to the
exploited transmit diversity for C/I < 5 dB. The performance characteristics are the same for halved and
cyc
full transmit power. The pure macro diversity scenario ( δ
1
= 0 ) at C/I = 0 dB also represents the
conventional OFDMA single-user case without any inter-cellular interference. The cell sites benefit from
the halved transmit power for the used C-CDD sub-carriers because a reduction of the inter-cellular
interference is achieved.
The drawback of the basic C-CDD principle, namely the artificial delay and the inherent delay cancel out
each other, can be eliminated by an adaptive approach using randomised delays out of a defined interval.
This needs no additional signalling to the receiver. The delays are chosen out of the interval defined by
S=[ Δ
cyc
δ
cyc
, δ
max
] starting with
cyc
Δ δ which excludes the delays causing the performance degradation
cyc
(e.g., at 4.7 dB, see Figure H-2). δ max
represents the maximum chosen delay. Furthermore, there is the
possibility to include a power adaptation to the C-CDD principle. In contrast to the randomised delays
this will need a feedback of the current C/I situation to the receiver. The power of the n cells follows the
constraint:
NBS −
∑
1 Pn
= Pmax
n=
0
. The maximum transmit power is normalised to one in the following
simulations. The proposed schemes are illustrated in Figure H-1 within the block diagram.
Applying the new randomised choice of delays and the power adaptation to the system results in the
performance curves shown in Figure H-3. Each BS is restricted to a maximum transmit power in a
regulated cellular system. We assume two cases: first, both BSs will transmit at most P max
/2
simultaneously; secondly, one BS can transmit at most P max
. For the first case ( P /2
n
≤ Pmax ), the dotted
line represents the performance by using the adaptive power factor throughout the cell border area. The
received diversity and the resulting performance gain is increased in a wider area around the cell border.
The influence of the second BS vanishes for larger absolute values of C/I due to the propagation path loss
influence. Therefore, it is possible to softly switch on/off C-CDD. The performance degradation due at
C/I = -4.7 dB is still existent. This can be compensated with the combination of adaptive power and an
cyc
appropriate choice of the cyclic delays Δ δ = 25 in C-CDD. A further performance gain can be
achieved by the second case ( Pn
≤ Pmax
). Obviously due to the higher transmit power, the performance is
maximised over the whole cell border area.
C-CDD is desirable to use in the outer part of the cells, depending on available resources in adjacent cells.
It is also possible to apply C-CDD to three neighbouring cell sites. In [PD06], it was shown that C-CDD
is applicable for MC-CDMA.
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Figure H-3: BER versus C/I for SNR = 5 dB using adaptive power factors and in combination with
randomly chosen cyclic delays.
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Appendix I.
Macro diversity techniques for MBMS
I.1 Description
I.1.1
MBMS
The Multimedia Broadcast and Multicast Service (MBMS) is a unidirectional point to multipoint service:
data is transmitted from a single source to multiple recipients. MBMS can provide simultaneously
downlink services for multiple users in full area coverage without taking into account user location and
radio conditions. MBMS is seen as essential to support the full range of mobile TV and video services.
Macro diversity is considered as a possible enhancement to the MBMS. In a MBMS service the
transmitted content is expected to be network-specific rather than cell-specific. Therefore, a natural way
of improving the physical layer performance is to take advantage of macro diversity. Basically, the
diversity combining concept consists of receiving redundantly the same signal over two or more fading
channels, and combine these multiple replicas at the receiver in order to increase the overall received
SNR.
On the network side, this means ensuring sufficient time synchronisation of identical MBMS
transmissions in different cells; on UT side, this means the capability to receive and decode the same
content from multiple transmitters simultaneously.
The macro diversity can improve the quality of signal reception when the UT overcomes bad propagation
conditions. The main application domain is the proximity of the cell border. So the total emission power
corresponding to MBMS services (and the inter-cell interference) can be reduced in the entire cell.
I.1.2 Inter-cell interference mitigation based on macro diversity
In the downlink, two types of networks can be distinguished:
Multiple Frequency Network (MFN): several transmitters send almost simultaneously the same signal
over different frequency channels. As the base stations cannot be assumed to be time synchronised,
different receiver chains are needed to demodulate the signals from the distinct base stations. This is still
very complex. Two schemes can be considered for MFN:
• Hard combining scheme: the technique selects the best cell base station in terms of path-loss and
shadowing in order to further mitigate the adverse effects of interference,
• Soft combining scheme: for multi-cell broadcast, soft combining of radio links can be supported,
assuming a sufficient degree of inter-BS time synchronisation, at least among a subset of BSs.
Single Frequency Network (SFN): several transmitters send simultaneously the same signal over the
same frequency channel. The user can employ a single receiver to demodulate the superimposed signals
but the SFN gain is sensitive to the temporal synchronisation of the signals received from different BSs.
In fact, as for a single link, in order to avoid interference between OFDM symbols, the cyclic prefix
length must be longer than or equal to the maximum potential time spread of the multi-path fading
channel.
Table I-1 gives an overview of schemes studied in the following paragraphs:
Table I-1: Overview of the MFN/SFN complexity.
Multiple Frequency Network
Single Frequency Network
Combining scheme Soft combining Hard combining No combining needed
Receiver complexity Medium High Low
- receiver unit One per BS One per BS One
- receiver unit One per BS One per BS One
- decoding unit One One per BS One
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Firstly, the study aims to analyse the sensitivity of the MFN and SFN radio link performances w.r.t. the
path-loss difference and secondly, to evaluate the impact of time delay and frequency de-synchronisation
on the radio link performance.
I.2 Requirements
Macro diversity requires the base stations to be synchronised. One of the aims of the study is to evaluate
the amount of frequency and time synchronisation needed to achieve a given level of performance.
In terms of service, [3GPP25913] specifies for EMBMS a “cell edge spectrum efficiency of 1 bps/Hz
equivalent to the support at least 16 mobile TV channels at around 300 kbps per channel in a 5 MHz
carrier in an urban or suburban environment”. In this study, we adopt this performance requirement as the
baseline.
I.3 Evaluations
I.3.1 Transport channel multiplexing structure for MBMS
Data arrive to the coding/multiplexing unit in form of transport block sets once every 0.3456 ms. We have
assumed full FDD duplex so the receiver continually receives the MBMS. The transport channel
multiplexing structure for MBMS is shown in Figure I-1.
MBMS
(Mobile TV Channels)
CRC attachment
Channel coding
Rate matching
Interleaving
Digital Modulation
User Multiplexing
OFDMA Distribution
Framing
OFDM Modulation
Physical Channel
Figure I-1: Transport channel multiplexing structure for MBMS.
Error detection is provided on transport blocks through a Cyclic Redundancy Check (CRC). The size of
the CRC is set to 16 bits (not specified in WINNER so far). CRC is one of the hard macro diversity
criteria needed to select the decoded block to be passed to the MAC layer. Concerning the channel coding
module, the implemented coding scheme is the duo-binary turbo encoding. Rate matching means that bits
on a transport channel are repeated or punctured. The user multiplexing module reads the symbols of all
the users contained in one transmission time interval, and then re-arranges them using the B-EFDMA
scheme for the non-frequency adaptive schemes in the downlink. One chunk is decomposed in 8 blocks of
4 x 3 symbols and one pilot symbol is included within each block, if possible located near the centre of
the block, as illustrated in Figure I-2.
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S S
P S P S
S
S
S P P S
S S
S
P S P
S S
S S
P S P
S
P Pilots
S Signalisation
Figure I-2: Chunk structure (8 dedicated pilot symbols and 18 control symbols per chunk layer).
The OFDMA distribution module supports the multiple access schemes for non-frequency adaptive
downlinks: Block Equidistant Frequency Division Multiple Access (B-EFDMA) [WIN2D6137], as
illustrated in Figure I-3.
P P
…
…
…
…
…
…
…
…
…
…
…
…
…
Time
Frequency
Block Equidistant Frequency Division Multiple Access (B-EFDMA)
for MBMS
Figure I-3: Illustration of B-EFDMA resource allocation for MBMS.
The framing module maps and indexes the N physical channel symbols (sub-carriers) available in one
OFDM symbol according to the RF spectrum as illustrated in Figure I-4 below:
Indexing of N available physical sub-carriers
in one OFDM symbol in RF spectrum
F c
Negative
Positive
1
N
Figure I-4: Mapping of physical channel symbols in frequency domain.
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I.3.2
Combining algorithms for multiple frequency networks
I.3.2.1 Selective combining
Figure I-5 shows a simplified illustration of the macro diversity operations. Here, we consider inter-cell
site diversity between two-cell sites. The network simulcasts point to multipoint MBMS contents on the
MBMS, and the UT receives and decodes the MBMS data from multiple radio links simultaneously.
Selection of the radio link is performed on a transport block basis at the RLC, based on a selection
criterion.
F BS n°1
x
Frequency
Domain
Equalizer
Soft
Demapper
Channel
Decoding
CRC
F BS n°2
Mean Received
Power
SNR
LLR
Selective
Combining
x
Frequency
Domain
Equalizer
Soft
Demapper
Channel
Decoding
CRC
Figure I-5: Selective combining for MFN.
When the blocks corresponding to the two antennas are detected false, the best block can be chosen by
taking into account a second criterion, for example the SNR or the received power.
I.3.2.2 Soft combining
Figure I-6 shows a simplified illustration of the soft-combining operations.
F BS n°1
x
Maximum
Ratio
Combining
Soft
Demapper
Channel
Decoding
CRC
F BS n°2
x
Figure I-6: Soft combining for MFN.
I.3.3 Assumptions
Table I-2 gives the main physical link level simulation assumptions (compliant with WINNER
specifications [WIN2D6137]).
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Table I-2: Simulation assumptions.
System
FFT size 2048
Guard interval
3,2μs
Number of available carriers 1152
Number of chunk allocated to MBMS 16
Channel
Deployment
Wide Area
Profile
WINNER C2
Mobile speed
3 km/h
Path-loss difference
Static
Link
Channel Estimation
Perfect/realistic
Number of receive antennas 1 or 2
Service
MCS 3 4 5
Modulation QPSK QPSK QPSK
Code rate 0,5 2/3 3/4
Service
Rate [Mbps] 3,1 4,2 4,7
Spectral efficiency [bps/Hz] 0,6 0,8 1,0
Information block size [bits] 1088 1456 1640
CRC 16
I.3.4
Results
I.3.4.1 Multiple Frequency Network (MFN)
Figure I-7 shows the radio link performance for a service of 4.75 Mbps and a Winner C2 channel model
[WIN2D111]. The two radio links considered here are not attenuated by path-loss. A comparison of the
performance with and without macro diversity shows that macro diversity gives a substantial gain (about
2 dB for a BLER of 10 -2 for selection combining and about 6 dB for soft combining). The following
selection combining criteria are considered:
• SNR measured after frequency domain equaliser (FDE)
• CRC and mean received power (P r ) measured at antenna
• CRC and SNR
• Genie aided (the message is perfectly known at the receiver so the BER of a block can be
evaluated and the best block in terms of BER can be chosen).
The results show that all the criteria using CRC in the selection of the block are optimum in terms of
BLER.
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Figure I-7: MFN – MCS5 - BLER versus SNR.
Figure I-8 shows the radio link performance in terms of BER. The soft combining algorithm gives the
best performance (about 6 dB gain compared to a single link at BER 10 -3 ). The selections based on CRC
are optimal in terms of BLER and nearly optimal in terms of BER (the performance is very close to that
of genie-aided) but the complexity of these combining algorithms is high (2 blocks must be decoded
simultaneously). The less complex selection based on SNR has quite less attractive performance (0.5 dB
compared to the performance of selection with the aid of CRC), but gives a 1.3 dB gain compared to the
single link performance.
Figure I-8: MFN – MCS5 - BER versus SNR.
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Figure I-9 and Figure I-10 give the SNR target for BLER of 10 -2 and BER of 10 -3 , respectively, as a
function of the difference of the radio link path-loss (δ PL ).
Figure I-9: MFN – MCS5 - BLER Criteria – Impact of δ PL .
Figure I-10: MFN – MCS5 - BER Criteria – Impact of δ PL .
In both two cases the soft combining scheme gives the best performance. This can be explained by the
fact that the soft combining increases the SNR before decoding so increases the ability of code to correct
errors (see the raw BER difference between the single link and soft-combining in Figure I-8). If the δ PL is
more than -6 dB, the hard combining schemes are completely inefficient. Table I-3 gives the SNR target
obtained for different MCS.
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Table I-3: MFN performance for different MCS.
Scheme \ QoS Criteria BLER BER
MCS 3 MCS 4 MCS 5 MCS 3 MCS 4 MCS 5
Single Link n°1 6,5 9,8 11,9 6,7 9,7 11,5
Single Link n°2 6,5 9,9 12,0 6,8 9,8 11,6
Selection Combining based on SNR 5,1 8,5 10,6 5,2 8,3 10,2
Selection Combining based on CRC and Pr 4,8 8,0 9,9 5,0 8,0 9,7
Selection Combining based on CRC and SNR 4,8 8,0 9,9 5,0 7,9 9,7
Genie Aided 4,8 8,0 9,9 4,9 7,8 9,6
Soft Combining 1,4 4,0 5,5 1,5 3,9 5,2
Compared to the single link performance, the soft combining allows a gain of 5.1 dB for MCS3 and of 6.4
dB for MCS5, see Table I-4.
Table I-4: MFN performance gain for different MCS.
Scheme \ MCS MCS 3 MCS 4 MCS 5
Soft Combining -5,1 -5,8 -6,4
Selection Combining based on CRC and SNR -1,7 -1,8 -2,0
This can be explained by the difference of the code rate (1/2 for MCS3 and 3/4 for MCS5). The gain
brought by the soft combining scheme increases with the code rate. The macro diversity is more
favourable to the soft combining.
The performance obtained with realistic channel estimation is given in Figure I-11. Compared to the
perfect channel estimation, 2 dB degradation can be observed. Notice that close to the serving BS, the
performance of soft combining is slightly degraded (about 0.5 dB) compared to the other schemes. This is
due to the presence of the Gaussian noise of the second link.
Figure I-11: MFN – MCS5 – WINNER C2 path loss difference - Realistic channel estimation.
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I.3.4.2 Single Frequency Network (SFN)
The drawback of SFN is the increase of the length of the channel profile thus SFN transmission can be
considered as a severe form of multi-path propagation. The guard interval insertion between the symbols
can not provide a complete elimination of the inter-symbol interference (ISI) overhead due to SFN. The
gain of SFN depends on two main parameters:
• δ PL : The difference of path-loss between the 2 radio links.
• δ t : The difference of propagation times.
If δ t is greater than the guard interval, the second link interferes, as shown in Figure I-12 which illustrates
the inter-symbol interference.
Link n°1
D n (t+τ 0 )
CP
D n+1 (t+τ 0 )
CP
D n (t+τ 1 )
CP
D n+1 (t+τ 1 )
CP
D n (t+τ 2 )
CP
D n+1 (t+τ 2 )
CP
δ τ
ISI
D n (t+τ 0 )
CP
D n+1 (t+τ 0 )
CP
D n (t+τ 1 )
CP
D n+1 (t+τ 1 )
CP
D n (t+τ 2 )
CP
D n+1 (t+τ 2 )
CP
Link n°2
Figure I-12: SFN - Inter-symbol interference.
In [WIN2D111], the expression of the path loss is given by:
⎛ f ⎞
PL = ⎡
⎣44,9 − 6,55.log10 ( hbs
) ⎤
⎦log10 ( d ) + 34, 46 + 5,83log10 ( hbs
) + 20log10 ⎜ 9 ⎟
⎝5.10
⎠
This expression allows to set at each position of the cell the couple [δ PL, δ t ], see Figure I-13.
Figure I-13: WINNER C2 Profile of δ PL .
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Figure I-14 gives the SNR target for BER of 10 -3 as a function of δ t for perfect channel estimation (PCE)
and for realistic channel estimation (RCE). Notice that the intra-cell interference reduction is not taken
into account here. Close to the base station, the SFN and single link have the same performance. The
maximum gain is obtained at the cell edge. Notice that the performance obtained with realistic channel
estimation is degraded at 313 m or 1.25 μs (0.5 dB of degradation compared to the performance close to
the BS).
Figure I-14: SNR target for BER of 10 -3 versus δ t .
To analyse the performance of SFN, two parameters are introduced:
• The interference reduction: As the SFN makes use of the neighbouring MBMS transmissions;
the inter-cell interference from the neighbouring cells is transformed into useful signal. As a
consequence the inter-cell interference received by a UT in a SFN network is reduced.
No
Γ IR =
No
+ SNRPL .
• The diversity gain: this gain is equal to the difference of the SNR of the single link and the SNR
of SFN, both obtained with a normalised Gaussian noise (all other things being equal). As the
second link does not contribute here to interference, this gain is due to the diversity brought by
the second link.
( SNR) ( SNR)
Γ DG = −
SL SFN
Table I-5 summarises the different gains achieved by SFN. The first part of the table gives the parameter
associated with the UT location; the next part corresponds to the performance obtained with perfect
channel estimation (PCE) and with realistic channel estimation (RCE).
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Table I-5: SFN performance.
d δt δ PL SNR
PCE
Interference
reduction
Diversity gain
SNR
RCE
Interference
reduction
Diversity gain
m Sample μs dB dB dB dB dB dB dB
500 0 0,0 0,0 8,7 1,6 2,9 10,7 2,4 2,9
463 20 0,3 2,3 9,1 1,1 2,4 11,3 1,8 2,2
425 40 0,5 4,7 9,8 0,8 1,7 12,4 1,4 1,2
388 60 0,8 7,1 10,4 0,5 1,2 13,3 1,0 0,3
350 80 1,0 9,6 10,8 0,3 0,8 13,7 0,6 -0,1
313 100 1,3 12,2 11,2 0,2 0,4 14,1 0,4 -0,5
275 120 1,5 15,0 11,3 0,1 0,3 14,0 0,2 -0,4
238 140 1,8 18,1 11,4 0,1 0,2 13,9 0,1 -0,3
200 160 2,0 21,5 11,5 0,0 0,1 13,8 0,0 -0,2
163 180 2,3 25,5 11,5 0,0 0,0 13,7 0,0 -0,1
125 200 2,5 30,2 11,6 0,0 0,0 13,6 0,0 0,0
88 220 2,8 36,4 11,6 0,0 0,0 13,6 0,0 0,0
50 240 3,0 45,7 11,6 0,0 0,0 13,6 0,0 0,0
13 260 3,3 67,8 11,6 0,0 0,0 13,6 0,0 0,0
These results show that SFN is more favourable to services which need high SNR. In the case of realistic
channel estimation there is diversity gain degradation when the distance is comprised between 125 m and
350 m. This degradation can be explained by the sensibility of SFN to time synchronisation, as shown in
Figure I-15. A 0 dB pathloss difference was considered. As we can see, a time synchronisation higher
than 1μs induces a high degradation of the performance (note this 1μs delay between the received signals
is what is experienced at d = 350 m). The perfect channel estimation is less sensitive to this delay (the
performance degrades only when the delay is higher than 4 μs, which corresponds to the guard interval).
Figure I-15: Sensibility of SFN to time synchronisation.
Figure I-16 shows the sensibility of MFN to time synchronisation (here only realistic channel estimation
is considered). The advantage of hard combining scheme based on CRC and P r (received power) is that
the performances are constantly majored by the performance of the best link. When the SNR is used to
select the best block, the performance degrades. Compared to the SFN case, MFN with soft combining is
less sensitive to time synchronisation.
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Figure I-16: Sensibility of MFN to time synchronisation (SFN added).
In the case of RCE the performance is very sensitive to the time synchronisation error. This can be
explained by the impulse response of the channel as illustrated in Figure I-17.
a) Perfect time synchronisation b) Error of synchronisation (2,5 μs)
Figure I-17: Impulse response of the channel.
When the signals are not synchronised, the response of the channel is very perturbed so the quality of the
channel estimation is low and the performance of SFN decreases. The channel estimation is made with
these impulse responses. One chunk corresponds to 4 consecutive points of these responses. The
comparison of the two curves proves that a synchronisation error induces a degradation of the channel
estimation quality.
Figure I-18 summarises the MFN and SFN performance obtained for the WINNER C2 channel model. As
we can see, macro diversity starts improving the performance at 200 m from the cell edge for MFN with
soft combining and SFN, and only 100 m from the cell edge for MFN with hard combining. In terms of
performance, the MFN with soft combining overcomes all the other schemes. The MFN with soft
combining SNR target is about 1.5 dB better than the SFN SNR target but the MFN cost in terms of
occupied sub-carriers is twice. So there is a trade-off between performance and resource consumption,
and the choice of network will depend on the load of the cell (and in particular on the number of chunks
allocated to dedicated services). Compared to MFN, SFN is far more sensitive to the difference of
propagation durations (the channel estimation quality gets worse when this delay increases).
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Figure I-18: Performance comparison of SFN and MFN – Perfect channel estimation.
Figure I-19 summarises the MFN and SFN performance obtained for the WINNER C2 channel model and
realistic channel estimation. Compared to the SFN, the macro diversity area of MFN with soft combining
is almost doubled (175 m for SFN and 300 m for MFN).
Figure I-19: Performance comparison of SFN and of MFN – Realistic channel estimation.
Figure I-20 gives the MFN and SFN sensibility to the frequency offset obtained for the WINNER C2
channel model and realistic channel estimation. In the case of SFN, decision feedback channel estimation
is considered. Compared to the SFN case, MFN schemes are far more resistant. In the case of MFN hard
combining, the performance can be guaranteed.
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Figure I-20: Sensibility of MFN and SFN to frequency synchronisation offset.
The behaviour of MFN hard combining in presence of frequency synchronisation offset is completely
different of its behaviour in presence of time synchronisation offset. Figure I-21 shows an example of the
degradation due to different frequency synchronisation offsets in the case of an AWGN channel (high
SNR). The curves here represent the frequency response of the channel (the output of the FFT). With a
perfect synchronisation, the channel response is completely flat but the distortion increase with the
frequency synchronisation offset.
Figure I-21: Response of the AWGN channel for different frequency synchronisation offsets.
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