Code acquisition in direct sequence spread spectrum systems - Oulu
Code acquisition in direct sequence spread spectrum systems - Oulu
Code acquisition in direct sequence spread spectrum systems - Oulu
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PrefaceResearch work related to this thesis was carried out at the Centre for Wireless Communications(CWC) and Telecommunication laboratory, Department of Electrical andInformation Eng<strong>in</strong>eer<strong>in</strong>g, University of <strong>Oulu</strong>, <strong>Oulu</strong>, F<strong>in</strong>land, dur<strong>in</strong>g the years 2003-2009.I am very grateful to my supervisor Prof. Jari I<strong>in</strong>atti and Dr. Harri Saarnisaarifor their support and guidance throughout the whole research project. Special thanksbelongs also to my colleague Lic. Tech. Pekka Lilja for research co-operation. I wouldlike to thank Dr. Ian Oppermann, Prof. Matti Latva-aho and Lic. Tech. Ari Pouttu, the<strong>direct</strong>ors of CWC dur<strong>in</strong>g the course of this research work, and Prof. Pentti Leppänen,Head of the Telecommunication laboratory, for giv<strong>in</strong>g the possibility to do this work atthe CWC.I would like to thank the reviewers of this thesis, Prof. Erik Ström from the ChalmersUniversity of Technology, Göteborg, Sweden, and Dr. Marcos Katz from VTT TechnicalResearch Centre, <strong>Oulu</strong>, F<strong>in</strong>land, for their valuable work. Thanks belongs also to thewhole CWC and Telecommunication laboratory staff equally for a comfortable work<strong>in</strong>gatmosphere.The research fund<strong>in</strong>g provided by the Graduate School <strong>in</strong> Electronics, Telecommunicationand Automation (GETA), the F<strong>in</strong>nish Defence Forces, the F<strong>in</strong>nish Fund<strong>in</strong>gAgency for Technology and Innovation (Tekes), Nokia, Elektrobit, and Instrumento<strong>in</strong>tiare gratefully acknowledged. In addition, the follow<strong>in</strong>g F<strong>in</strong>nish foundationshave granted personal scholarships for my doctoral studies: Tekniikan edistämissäätiö,HPY:n tutkimussäätiö and Nokia Oyj:n säätiö. All these foundations earn the warmestthanks for their f<strong>in</strong>ancial support.I would like to express my deepest gratitude to my closest network, <strong>in</strong>clud<strong>in</strong>g myparents, brothers, parents-<strong>in</strong>-law, other relatives and friends. F<strong>in</strong>ally, I want to expressmy lov<strong>in</strong>g thanks to my wife Hannele for all the care, patience, and understand<strong>in</strong>g dur<strong>in</strong>gall these years, and to our little son Lauri, for br<strong>in</strong>g<strong>in</strong>g joy to our lives.<strong>Oulu</strong>, March 9, 2009Henri Puska7
Abbreviationsa(θ)a(θ 0 )a(θ i )a ST (θ)AB cohc(n)c(t)c(ζ ;t)deE b /N 0E c /N 0f ACQ (t)f cf di(t)i m (t)i(t)I/SLmMnn m (t)n(t)PP ACQP Bartlett (θ)P Capon (θ)P Dsteer<strong>in</strong>g vectorsteer<strong>in</strong>g vector toward desired signalsteer<strong>in</strong>g vector toward <strong>in</strong>terfer<strong>in</strong>g signalspace-time steer<strong>in</strong>g vectormatrix consist<strong>in</strong>g of steer<strong>in</strong>g vectorscoherence bandwidthdiscrete time <strong>spread</strong><strong>in</strong>g code <strong>sequence</strong>cont<strong>in</strong>uous time <strong>spread</strong><strong>in</strong>g code <strong>sequence</strong>time-variant impulse response of radio channelcross-correlation vector used <strong>in</strong> the MMSE algorithmconstra<strong>in</strong>t vector used <strong>in</strong> nullsteer<strong>in</strong>g algorithmsignal-to-noise ratio per bitsignal-to-noise ratio per chipprobability density function of <strong>acquisition</strong> timecarrier frequencyDoppler shift between the transmitter and receiver<strong>in</strong>terfer<strong>in</strong>g signal<strong>in</strong>terfer<strong>in</strong>g signal at mth antenna elementvector of <strong>in</strong>terfer<strong>in</strong>g signals <strong>in</strong> all antennas<strong>in</strong>terference-to-signal ratiocode lengthmth antenna elementnumber of antenna elementsdiscrete time variableadditive white Gaussian noise at mth antenna elementvector of noise signals <strong>in</strong> all antennasnumber of time taps <strong>in</strong> space-time beamform<strong>in</strong>gprobability of <strong>acquisition</strong>spatial <strong>spectrum</strong> us<strong>in</strong>g Bartlett’s DOA estimationspatial <strong>spectrum</strong> us<strong>in</strong>g Capon’s DOA estimationprobability of detection9
PDovP FAP missP MUSIC (θ)P ovmissRˆRR INR STˆR STStT ACQT cT cohT dT FAT HT MAT MA_maxT MA_m<strong>in</strong>T sT stopV nw mww STWx(t)x m (n)x m (t)x(n)x(t)x ST (n)y(n)overall probability of detectionprobability of false alarmprobability of missspatial <strong>spectrum</strong> us<strong>in</strong>g the MUSIC algorithmoverall probability of missarray correlation matrixestimate of correlation matrixcorrelation matrix consist<strong>in</strong>g of <strong>in</strong>terference and noise.space-time correlation matrixestimate of space-time correlation matrix<strong>in</strong>formation signal powercont<strong>in</strong>uous time variable<strong>acquisition</strong> timechip timechannel coherence timedwell timefalse alarm penalty timedetection thresholdmean <strong>acquisition</strong> timemaximum expected <strong>acquisition</strong> timem<strong>in</strong>imum expected <strong>acquisition</strong> timesymbol timestopp<strong>in</strong>g timenoise subspaceadjustable weight coefficient at mth antenna elementweight coefficient vectorspace-time weight coefficient vectortransmitted signal bandwidthreceived signalreceived discrete time signal at mth antenna elementreceived cont<strong>in</strong>uous time signal at mth antenna elementvector of received discrete time signals <strong>in</strong> all antennas, i.e., snapshotvector of received cont<strong>in</strong>uous time signals <strong>in</strong> all antennasspace-time sample vectorarray output signal10
y m (n)zz(τ i )z Iz Qdiscrete time MF output signal at mth antenna elementdecision variable at the detector outputdecision variable at the detector output as a function of delaydecision variable at I-branch of the detector outputdecision variable at Q-branch of the detector outputΔζθθ 0θ 1 ,···,θ Kσ 2 T ACQτˆτˆτ MLτ iφ mφ mis the maximum time delay from the first to last antenna elementpropagation delayangle of arrivalangle of arrival of desired signalangles of arrivals of <strong>in</strong>terfer<strong>in</strong>g signalsvariance of <strong>acquisition</strong> timerelative delay between received and local <strong>spread</strong><strong>in</strong>g codesestimate of τmaximum likelihood estimate of τa code phase which is presently under <strong>in</strong>vestigationcarrier phase shift at the reference antennacarrier phase shift at the mth antenna elementA/DADLAICAWGNBERBFBF+MFBPSKCDMACFARCMECWDOADCDSDS/CDMAanalog-to-digital conversionadaptive diagonal load<strong>in</strong>gAkaike <strong>in</strong>formation theoretic criteriaadditive white Gaussian noisebit error ratebeamform<strong>in</strong>gs<strong>in</strong>gle MF is located after BF unitb<strong>in</strong>ary phase shift key<strong>in</strong>gcode division multiple accessconstant false alarm rateconsecutive mean excisioncont<strong>in</strong>uous wave<strong>direct</strong>ion-of-arrivaldifferentially coherent<strong>direct</strong> <strong>sequence</strong><strong>direct</strong> <strong>sequence</strong> code division multiple access11
DS/SSESPRITFCMEFDMAFFTFHFIRGLRTGSCGSMIICICIIFLMSLOSMAIMaxSINRMDLMFMF+BFMLMSEMMSEMUSICMVDRNLOSPDFPDIPGPNQRFRMSESDMA<strong>direct</strong> <strong>sequence</strong> <strong>spread</strong> <strong>spectrum</strong>estimation of signal parameters via rotational <strong>in</strong>variance techniquesforward consecutive mean excisionfrequency division multiple accessfast Fourier transformfrequency hopp<strong>in</strong>gf<strong>in</strong>ite impulse responsegeneralized likelihood ratio testgeneralized sidelobe cancellerglobal system for mobile communications<strong>in</strong>-phase -branch<strong>in</strong>terference cancellation<strong>in</strong>ter chip <strong>in</strong>terference<strong>in</strong>termediate frequencyleast mean squarel<strong>in</strong>e-of-sightmultiple-access <strong>in</strong>terferenceMaximum SINRm<strong>in</strong>imum description lengthmatched filterMF is located <strong>in</strong> each antenna element before BF unitmaximum likelihoodmean square errorm<strong>in</strong>imum mean square errormultiple signal classificationm<strong>in</strong>imum variance distortionless responsenon-l<strong>in</strong>e-of-sightprobability density functionpost detection <strong>in</strong>tegrationprocess<strong>in</strong>g ga<strong>in</strong>pseudo noisequadrature-branchradio frequencyroot mean square errorspace division multiple access12
List of orig<strong>in</strong>al articlesThis thesis is based on the follow<strong>in</strong>g n<strong>in</strong>e orig<strong>in</strong>al articles (I–IX), which are referred to<strong>in</strong> the text by their Roman numerals:I Puska H, Saarnisaari H & I<strong>in</strong>atti J (2003) Utiliz<strong>in</strong>g beamformers as <strong>in</strong>terference cancellers<strong>in</strong> code synchronization of DS/SS system. Proc of IEEE F<strong>in</strong>nish Signal Process<strong>in</strong>g Symposium,Tampere, F<strong>in</strong>land, on CD: 5p.II Puska H, Saarnisaari H & I<strong>in</strong>atti J (2005) Comparison of antenna array algorithms <strong>in</strong> DS/SScode <strong>acquisition</strong> with jamm<strong>in</strong>g. Proc. of IEEE Military Communication Conference, AtlanticCity, USA, 4: 2074–2080.III Puska H & Saarnisaari H (2004) Performance comparison of robust array algorithms <strong>in</strong> delayestimation. Proc. of IEEE Sensor Array and Multichannel Signal Process<strong>in</strong>g Workshop,Barcelona, Spa<strong>in</strong>, 1: 332–336.IV Puska H, Saarnisaari H & I<strong>in</strong>atti J (2005) Comparison of matched filter <strong>acquisition</strong> us<strong>in</strong>gbeamform<strong>in</strong>g and CME algorithm <strong>in</strong> impulsive <strong>in</strong>terference. Proc. of IEEE VehicularTechnology Conference (Spr<strong>in</strong>g), Stockholm, Sweden, 3: 1988–1992.V Puska H, Saarnisaari H, I<strong>in</strong>atti J & Lilja P (2007) Synchronization probabilities us<strong>in</strong>g conventionaland MVDR beam form<strong>in</strong>g with DOA errors. Proc. of IEEE Personal Indoor andMobile Radio Communication, Athens, Greece, on CD: 5p.VI Puska H, Saarnisaari H, I<strong>in</strong>atti J & Lilja P (2008) Serial search code <strong>acquisition</strong> us<strong>in</strong>g smartantennas with s<strong>in</strong>gle correlator or matched filter. IEEE Transactions on Communications56(2): 299–308.VII Puska H, I<strong>in</strong>atti J & Saarnisaari H (2009) Serial search and maximum selection basedcode <strong>acquisition</strong> techniques for s<strong>in</strong>gle and multi antenna <strong>systems</strong>. IEEE Transactions onWireless Communications 8(3). In press.VIII Puska H, Saarnisaari H & I<strong>in</strong>atti J (2005) An iterative method for code <strong>acquisition</strong> us<strong>in</strong>gDOA estimation and beamform<strong>in</strong>g. Proc. of IEEE Personal Indoor and Mobile RadioCommunication, Berl<strong>in</strong>, Germany, 2: 1165–1169.IX Puska H, Saarnisaari H, I<strong>in</strong>atti J & Lilja P (2007) Performance comparison of DS/SS code<strong>acquisition</strong> us<strong>in</strong>g MMSE and MVDR beamform<strong>in</strong>g <strong>in</strong> jamm<strong>in</strong>g. Proc. of IEEE MilitaryCommunication Conference, Orlando, USA, on CD: 7p.15
ContentsAbstractPreface 7Abbreviations 9List of orig<strong>in</strong>al articles 15Contents 171 Introduction 191.1 Background . ........................................................191.2 Motivation . . . . ......................................................201.3 Author’s contribution. ................................................221.4 Outl<strong>in</strong>e of the thesis ..................................................232 Literature review of code <strong>acquisition</strong> 252.1 Signal model . .......................................................252.2 Basic methods for code <strong>acquisition</strong> . ...................................262.2.1 Search strategies . .............................................262.2.2 Detector structures.............................................272.3 Performance measures. ...............................................322.4 Performance results <strong>in</strong> various environments . ...........................342.4.1 Fad<strong>in</strong>g channels . ..............................................352.4.2 Data modulation ..............................................382.4.3 Doppler effect. . . ..............................................382.4.4 Intentional <strong>in</strong>terference ........................................392.4.5 Multiple-access <strong>in</strong>terference . ...................................412.4.6 Other system <strong>in</strong>terference . .....................................422.4.7 Multiple antennas .............................................423 Smart antennas 473.1 Digital beamform<strong>in</strong>g algorithms . ......................................473.1.1 Classical beamform<strong>in</strong>g . ........................................483.1.2 Nullsteer<strong>in</strong>g ..................................................483.1.3 M<strong>in</strong>imum Variance Distortionless Response. .....................493.1.4 MaxSINR ....................................................503.1.5 Whiten<strong>in</strong>g filter ...............................................5017
3.1.6 M<strong>in</strong>imum Mean Square Error ...................................513.1.7 Space-time beamform<strong>in</strong>g . . . . ...................................513.1.8 Beam-space techniques ........................................533.2 DOA estimation algorithms . ..........................................543.2.1 Conventional techniques .......................................543.2.2 Subspace based techniques . ....................................553.2.3 Maximum likelihood techniques . ...............................563.2.4 Detection of number of signals. .................................564 Summary of the orig<strong>in</strong>al articles 594.1 General . . ...........................................................594.2 <strong>Code</strong> <strong>acquisition</strong> performance under <strong>in</strong>tentional <strong>in</strong>terference .............594.3 Analysis of synchronization probabilities and mean <strong>acquisition</strong> timeswith smart antennas . .................................................635 Conclusion and discussion 75References 77Orig<strong>in</strong>al articles 8918
1 Introduction1.1 BackgroundThe demand of wireless communications solutions has been <strong>in</strong>creased dur<strong>in</strong>g the lastfew decades. This demand has brought along a totally new bus<strong>in</strong>ess opportunities whichhave had positive effects on many economies. In order to fulfil more demand<strong>in</strong>g communicationrequirements, a trend <strong>in</strong> the development of communication <strong>systems</strong> hastaken place from analog to digital <strong>systems</strong>. Cellular <strong>systems</strong> have been digital s<strong>in</strong>ce the90’s, when mobile phones started to use the second generation standard, Global Systemfor Mobile Communications (GSM). Recently, the TV broadcast<strong>in</strong>g system has alsochanged from analog to digital e.g., <strong>in</strong> F<strong>in</strong>land.In a digital communication system, the orig<strong>in</strong>al analog <strong>in</strong>formation, like speech, isfirst analog-to-digital (A/D) converted, then compressed and segmented <strong>in</strong>to smallerblocks of data. In the transmitter, each data block proceeds through the protocol stackwhere each layer <strong>in</strong>serts its own layer <strong>in</strong>formation to the orig<strong>in</strong>al end user data. Exactlythe same protocol stack is sited also <strong>in</strong> the receiver, because the protocols def<strong>in</strong>ecommon rules for communication. The lowest level of the protocol stack is a physicallayer whose task is to carry all <strong>in</strong>formation com<strong>in</strong>g from upper layers to the receiveras reliably as possible. In wireless communication <strong>systems</strong>, the physical layer datawhich consists of the orig<strong>in</strong>al end user data, protocol <strong>in</strong>formation, and possible control<strong>in</strong>formation is transmitted via electromagnetic radiation on a radio frequency (RF)carrier. Because there are multiple users and only one common transmission media,there must be some means (i.e., multiple access methods) to separate those users. Basically,there are four different multiple access methods: frequency division multipleaccess (FDMA), time division multiple access (TDMA), code division multiple access(CDMA), and space division multiple access (SDMA). Multiple access methods canalso be comb<strong>in</strong>ed (i.e., hybrid approach). This thesis concentrates ma<strong>in</strong>ly on CDMA<strong>systems</strong>, i.e., <strong>spread</strong> <strong>spectrum</strong> (SS) <strong>systems</strong>, where a <strong>spread</strong><strong>in</strong>g <strong>sequence</strong> is used toseparate users and <strong>spread</strong> the <strong>spectrum</strong> of the transmitted signal. Also smart antennasare covered, which SDMA is based on. In SDMA, multiple users are separated <strong>in</strong> aspatial doma<strong>in</strong>. Smart antennas (also known as adaptive antennas) are antenna arrayswith smart signal process<strong>in</strong>g algorithms used to identify spatial properties, such as the19
<strong>direct</strong>ion-of-arrival (DOA) of the signal. It uses this <strong>in</strong>formation for beamform<strong>in</strong>g, i.e.,for shap<strong>in</strong>g the radiation pattern of the antenna array. However, the ma<strong>in</strong> contributionis <strong>in</strong> the code synchronization of <strong>spread</strong> <strong>spectrum</strong> signals us<strong>in</strong>g smart antennas. <strong>Code</strong>synchronization means time, i.e., delay synchronization and it consists of two phases:code <strong>acquisition</strong> (<strong>in</strong>itial/coarse synchronization) and code track<strong>in</strong>g (f<strong>in</strong>e synchronization)of which the <strong>in</strong>itial process is considered <strong>in</strong> this thesis. The studied <strong>acquisition</strong>methods are applicable to the other multiple access methods too, if those methods havea separate <strong>spread</strong><strong>in</strong>g <strong>sequence</strong> for synchronization purposes. The most commonly usedtechniques <strong>in</strong> <strong>spread</strong> <strong>spectrum</strong> communications are <strong>direct</strong>-<strong>sequence</strong> (DS) and frequencyhopp<strong>in</strong>g (FH) modulation schemes and their hybrids (DS/FH). In <strong>direct</strong> <strong>sequence</strong> <strong>spread</strong><strong>spectrum</strong> (DS/SS) <strong>systems</strong>, which is <strong>in</strong>vestigated <strong>in</strong> this thesis, each <strong>in</strong>formation symbol<strong>in</strong> a physical layer is multiplied by a <strong>spread</strong><strong>in</strong>g code which consists of L chips. The<strong>spectrum</strong> <strong>spread</strong>s because one <strong>in</strong>formation symbol is divided <strong>in</strong>to L chips, result<strong>in</strong>g <strong>in</strong> apulse rate which is L times as high as without DS <strong>spread</strong><strong>in</strong>g. The receiver gets a benefitaga<strong>in</strong>st <strong>in</strong>terfer<strong>in</strong>g signals from DS <strong>spread</strong><strong>in</strong>g at the de<strong>spread</strong><strong>in</strong>g (i.e., <strong>spectrum</strong> narrow<strong>in</strong>g)phase. The amount of this benefit is equal to L and it is called the process<strong>in</strong>g ga<strong>in</strong>(PG) of the DS/SS system.In order to recover the orig<strong>in</strong>al end user <strong>in</strong>formation from the transmitted RF carrier,it is obvious that the communication system requires synchronization at many differentlevels. Because <strong>in</strong>formation is transmitted <strong>in</strong> a carrier, the receiver needs carrier synchronizationwhere the frequency and phase of the received signal are estimated. In<strong>spread</strong> <strong>spectrum</strong> <strong>systems</strong>, code synchronization is needed to de<strong>spread</strong> the received signal.In order to make symbol decisions, knowledge of the start<strong>in</strong>g and end<strong>in</strong>g time ofthe symbol is required which is called symbol synchronization. Because <strong>in</strong>formationsymbols are transmitted <strong>in</strong> frames, the receiver needs also a knowledge of the start<strong>in</strong>gand end<strong>in</strong>g po<strong>in</strong>t of each frame. After frame synchronization, the receiver is able toremove protocol stack overhead from different layers so that the orig<strong>in</strong>al transmittedend user <strong>in</strong>formation can be delivered to the recipient. Sometimes the communicationsystem also needs network synchronization if the number of nodes <strong>in</strong> the network use acommon tim<strong>in</strong>g reference.1.2 MotivationThe high data rate, capacity, and coverage targets of future wireless communications<strong>systems</strong> require vast improvements <strong>in</strong> all the layers of wireless networks. It has been20
shown that the use of multiple antennas will improve communication system performanceremarkably. Therefore, multiple antennas will be present <strong>in</strong> future wireless communication<strong>systems</strong>. Most of the multiple antenna research is concentrated <strong>in</strong> enhanc<strong>in</strong>gl<strong>in</strong>k- or network-level performance figures. It is worth <strong>in</strong>vestigat<strong>in</strong>g their potential alsoto improve the code synchronization performance of DS/SS <strong>systems</strong>. This topic hasalready been studied <strong>in</strong> some papers. Multiple antennas can be used either <strong>in</strong> a diversitymode or <strong>in</strong> a beamform<strong>in</strong>g mode, depend<strong>in</strong>g on the <strong>in</strong>terelement spac<strong>in</strong>g <strong>in</strong> the antennaarray. If antenna elements are widely spaced, then diversity techniques can be used(Rick & Milste<strong>in</strong> 1997, Park & Oh 1998, Ikai et al. 1999, Yang et al. 1999, Chang et al.2000, Ryu et al. 2002, Je et al. 2003, Sh<strong>in</strong> & Lee 2003, Kwon et al. 2006, Kwon et al.2007). If antenna elements are closely spaced, then beamform<strong>in</strong>g techniques can beapplied. The approach based on beamform<strong>in</strong>g is considered <strong>in</strong> this thesis.One of the earliest papers deal<strong>in</strong>g with code <strong>acquisition</strong> us<strong>in</strong>g beamform<strong>in</strong>g hasbeen published by Compton, who experimentally <strong>in</strong>vestigated the characteristics of anadaptive analog array <strong>in</strong> the presence of <strong>in</strong>terference (Compton 1978). A code <strong>acquisition</strong>structure, where only a s<strong>in</strong>gle correlator is needed after the beamform<strong>in</strong>g unit,was proposed by Katz (Katz et al. 2001b). There<strong>in</strong>, the authors <strong>in</strong>vestigated a beamform<strong>in</strong>gstructure that has as many fixed beams as there are antenna elements <strong>in</strong> thearray. In particular, the basic idea was to divide the whole angular uncerta<strong>in</strong>ty (360 ◦ )<strong>in</strong>to small angular cells us<strong>in</strong>g a simple beamform<strong>in</strong>g technique like a Butler matrix.The result was a two dimensional (2-D) serial search strategy which provides both delayand <strong>direct</strong>ion-of-arrival (DOA) estimates. The study was expanded to cover alsononuniform spatial distributions of <strong>in</strong>terference <strong>in</strong> (Katz et al. 2000, Katz et al. 2001a,Katz et al. 2004). The results showed that the <strong>acquisition</strong> performance was clearly degraded<strong>in</strong> this new scenario if a simple beamform<strong>in</strong>g algorithm is used. This is becausea simple beamformer offers only limited tolerance aga<strong>in</strong>st <strong>in</strong>terference. In those papers,the disturbance effects of <strong>in</strong>terference were reduced us<strong>in</strong>g a different search strategy,adaptive <strong>in</strong>tegration time and adaptive threshold sett<strong>in</strong>g schemes, <strong>in</strong>stead of apply<strong>in</strong>g<strong>in</strong>terference resistant beamform<strong>in</strong>g, i.e., advanced beamform<strong>in</strong>g. Therefore, there is aneed to expand the two-dimensional (delay – angle) code <strong>acquisition</strong> study to cover alsoadvanced beamform<strong>in</strong>g algorithms.Even though there are many papers concern<strong>in</strong>g <strong>in</strong>terference cancellation us<strong>in</strong>g smartantennas (see Godara 1997, and references there<strong>in</strong>), there are not many papers which<strong>in</strong>vestigate code <strong>acquisition</strong> performance under <strong>in</strong>terference when the advanced beamform<strong>in</strong>gtechniques are used. This thesis expands multi-antenna code <strong>acquisition</strong> re-21
search by <strong>in</strong>troduc<strong>in</strong>g an approach where DOA estimation is used to reduce the numberof angular cells <strong>in</strong> the hope of a reduced <strong>acquisition</strong> time. The <strong>in</strong>vestigated code <strong>acquisition</strong>methods (matched filter (MF) <strong>acquisition</strong>, serial search, maximum selection,verification) are well known <strong>in</strong> a s<strong>in</strong>gle antenna case (e.g., Polydoros & Weber 1984a,I<strong>in</strong>atti 2000a), but there is a lack of analysis <strong>in</strong> the multi-antenna case where DOA<strong>in</strong>formation is utilized.The receiver structure where a correlator is used <strong>in</strong> each antenna branch was studied<strong>in</strong> (Wang & Kwon 2000a, Wang & Kwon 2000b, Wang & Kwon 2003b and Wang &Kwon 2003a). In those papers, the outputs of correlators are comb<strong>in</strong>ed us<strong>in</strong>g beamform<strong>in</strong>gand the weights of the beamformer are obta<strong>in</strong>ed via least mean square (LMS)adaptation us<strong>in</strong>g a pilot channel. Another code <strong>acquisition</strong> scheme which is based onm<strong>in</strong>imum mean square error (MMSE) beamform<strong>in</strong>g was presented <strong>in</strong> (Zhang et al. 2003and Zhang et al. 2004). In those papers, beamform<strong>in</strong>g weights are also obta<strong>in</strong>ed via apilot channel, and code correlators <strong>in</strong>stead of matched filters are used. MMSE beamform<strong>in</strong>gis an attractive method, s<strong>in</strong>ce it does not need DOA <strong>in</strong>formation. MMSE beamform<strong>in</strong>gcan be used <strong>in</strong> <strong>in</strong>itial code <strong>acquisition</strong> without pilot symbols if only one periodof the known pseudo noise (PN) <strong>spread</strong><strong>in</strong>g code is used as a reference signal. This particularscheme is studied <strong>in</strong> this thesis such that there is a MF <strong>in</strong> each antenna branch,s<strong>in</strong>ce it is well known that MF-based <strong>acquisition</strong> is faster than correlator-based. Analysisof the mean <strong>acquisition</strong> time for MMSE beamform<strong>in</strong>g is also derived <strong>in</strong> order tomake performance comparisons between MMSE and the above-mentioned delay-anglesearch methods.1.3 Author’s contributionThe first ma<strong>in</strong> contribution of this thesis is to study how different beamform<strong>in</strong>g techniquescan be used for fight<strong>in</strong>g aga<strong>in</strong>st <strong>in</strong>tentional <strong>in</strong>terference dur<strong>in</strong>g the code <strong>acquisition</strong>process (Articles I-IV). A radiation pattern can be shaped via beamform<strong>in</strong>g so thatmaximum ga<strong>in</strong> is <strong>direct</strong>ed toward the desired signal and m<strong>in</strong>imum ga<strong>in</strong>s toward <strong>in</strong>terfer<strong>in</strong>gsignals. Because <strong>in</strong>terference suppression is performed <strong>in</strong> a spatial doma<strong>in</strong>, alsowideband <strong>in</strong>terfer<strong>in</strong>g signals can be suppressed. The root mean square error (RMSE)of the time delay estimate has been used as a performance measure. Results show thatmost spatial beamform<strong>in</strong>g algorithms are capable of cancell<strong>in</strong>g multiple different typesof <strong>in</strong>terfer<strong>in</strong>g signals. If <strong>in</strong>terfer<strong>in</strong>g and desired signals are arriv<strong>in</strong>g from the same <strong>direct</strong>ion(i.e., ma<strong>in</strong>lobe <strong>in</strong>terference), then a more complex space-time process<strong>in</strong>g must be22
applied. Also, the structures where a separate <strong>in</strong>terference cancellation unit is locatedeither before or after the beamformer are <strong>in</strong>vestigated as possible solutions aga<strong>in</strong>st ma<strong>in</strong>lobe<strong>in</strong>terference.The second ma<strong>in</strong> contribution of the thesis focuses on the theoretical analysis ofsynchronization probabilities and mean <strong>acquisition</strong> times (Articles V-IX). There<strong>in</strong>, alsothe DOA of the desired signal is unknown, whereas it is assumed to be known <strong>in</strong> thefirst group of articles. The effects of DOA errors on the code synchronization probabilitiesand mean <strong>acquisition</strong> times are <strong>in</strong>vestigated. Research work of the delay-anglecode <strong>acquisition</strong> (Katz et al. 2001b) is extended to cover also the advanced beamform<strong>in</strong>gtechniques, because they offer <strong>in</strong>terference suppression capability. Also a methodwhere the DOAs of arriv<strong>in</strong>g signals are estimated prior to the <strong>acquisition</strong> process isproposed. A model for code <strong>acquisition</strong> us<strong>in</strong>g DOA estimation and beamform<strong>in</strong>g ispresented and an expression for the mean <strong>acquisition</strong> time is derived, tak<strong>in</strong>g <strong>in</strong>to accountall the actions related to array signal process<strong>in</strong>g. Also a MMSE beamform<strong>in</strong>gstructure, where only one period of the known PN <strong>spread</strong><strong>in</strong>g code is used as a tra<strong>in</strong><strong>in</strong>gsignal is proposed. This structure avoids a two-dimensional delay-angle search and ithas, therefore, a smaller mean <strong>acquisition</strong> time.This thesis is based on n<strong>in</strong>e orig<strong>in</strong>al articles <strong>in</strong>cluded as Appendices. The basicidea of us<strong>in</strong>g beamform<strong>in</strong>g as an aid<strong>in</strong>g device was given by Dr. Harri Saarnisaariwho has done a lot of research work <strong>in</strong> the <strong>in</strong>terference suppression area. Prof. JariI<strong>in</strong>atti <strong>in</strong>troduced the author to the theory of code <strong>acquisition</strong>. All the papers havebeen done as team work where, however, the author of this thesis contributed as thema<strong>in</strong> author. The author developed the theoretical models, derived the performanceexpressions, performed numerical analysis and analyzed the results <strong>in</strong> all publishedpapers.1.4 Outl<strong>in</strong>e of the thesisThe thesis is organized as follows. Chapter 2 gives an overview of code <strong>acquisition</strong>literature. It describes commonly used code <strong>acquisition</strong> methods and gives the basicsof the different performance measures used <strong>in</strong> code <strong>acquisition</strong>. In addition, the performanceresults of code <strong>acquisition</strong> under various environments are reviewed. Chapter 3gives an <strong>in</strong>troduction to smart antennas, <strong>in</strong> which especially digital beamform<strong>in</strong>g andDOA estimation algorithms are covered. Chapter 4 concentrates on the author’s ownresearch and therefore, a summary of the orig<strong>in</strong>al papers is presented there<strong>in</strong>. F<strong>in</strong>ally,23
conclusions of the ma<strong>in</strong> results of the thesis are given <strong>in</strong> Chapter 5, where also somepossible <strong>direct</strong>ions for further research are presented.24
position of the received code phase is estimated. After code synchronization, the receiveris able to remove DS <strong>spread</strong><strong>in</strong>g by multiply<strong>in</strong>g the received signal with the localsynchronized reference <strong>spread</strong><strong>in</strong>g code c(t − ˆτ), where ˆτ is an estimate of τ. This procedureis denom<strong>in</strong>ated as de<strong>spread</strong><strong>in</strong>g <strong>in</strong> <strong>spread</strong> <strong>spectrum</strong> communication literature. Thecode synchronization process is typically split <strong>in</strong>to two stages: code <strong>acquisition</strong> wherean <strong>in</strong>teger part of τ/T c (coarse synchronization) and code track<strong>in</strong>g where a fractionalpart of τ/T c is estimated (f<strong>in</strong>e adjustment). Normalization factor T c is the chip time.S<strong>in</strong>ce this thesis concentrates on code <strong>acquisition</strong>, the next section will describe how acoarse estimate of τ/T c can be found.2.2 Basic methods for code <strong>acquisition</strong>2.2.1 Search strategiesThe term search strategy is used when we are talk<strong>in</strong>g about the strategy to f<strong>in</strong>d the correctcode phase. If there is not a priori <strong>in</strong>formation about τ, the tim<strong>in</strong>g uncerta<strong>in</strong>ty isthe whole <strong>spread</strong><strong>in</strong>g code period. Thus, if a <strong>spread</strong><strong>in</strong>g code consists of L chips, thenthe tim<strong>in</strong>g uncerta<strong>in</strong>ty is LT c , depicted <strong>in</strong> Fig. 1. In code <strong>acquisition</strong>, the whole tim<strong>in</strong>guncerta<strong>in</strong>ty area is divided <strong>in</strong>to f<strong>in</strong>ite number of small delay cells, and the quantized tim<strong>in</strong>guncerta<strong>in</strong>ty region is def<strong>in</strong>ed as the total number of those delay cells to be searched.Therefore, the size of the quantized tim<strong>in</strong>g uncerta<strong>in</strong>ty region depends on the quantiz<strong>in</strong>g<strong>in</strong>terval, which may be selected to be equal to the T c but it can be also smaller.Fig 1. Time uncerta<strong>in</strong>ty region.LT c1 2 3 ··· ··· LT ccorrect cellThe most typically used search strategies for code <strong>acquisition</strong> are the serial search andparallel search schemes. Classification <strong>in</strong>to parallel and serial search can be made accord<strong>in</strong>gto how many cells are <strong>in</strong>vestigated <strong>in</strong> parallel. If the number of simultaneously26
<strong>in</strong>vestigated cells is higher than one, then the strategy used is refered to as a parallelsearch and otherwise a serial search. In an extreme parallel search scheme, all the cells<strong>in</strong>side an uncerta<strong>in</strong>ty region are <strong>in</strong>vestigated simultaneously. This method has been studied<strong>in</strong> (Milste<strong>in</strong> et al. 1985, Davisson & Flikkema 1988, Cheng 1988). It will largelyreduce the <strong>acquisition</strong> time at the expense of an <strong>in</strong>creased implementation complexityof the receiver, because there must be one correlat<strong>in</strong>g element per each uncerta<strong>in</strong>ty cell.However, it is optimal <strong>in</strong> the sense that it f<strong>in</strong>ds a correct delay cell as fast as possible.The number of parallel correlat<strong>in</strong>g elements can also be smaller than was used <strong>in</strong> theprevious method. Such a scheme has been studied <strong>in</strong> (Sourour & Gupta 1989, Sourour& Gupta 1992, Chawla & Sarwate 1994, Sr<strong>in</strong>ivasan & Sarwate 1996).The serial search scheme is a very commonly used <strong>acquisition</strong> strategy, because onlya s<strong>in</strong>gle correlat<strong>in</strong>g element is needed there. In the serial search scheme cells <strong>in</strong>side anuncerta<strong>in</strong>ty region are <strong>in</strong>vestigated serially <strong>in</strong> a predeterm<strong>in</strong>ed order until a correct oneis found. The decision of the correct cell is made us<strong>in</strong>g a detector, where the decisionvariable is compared to a detection threshold. If the threshold is exceeded, then the<strong>in</strong>vestigated cell is considered as a correct one. The cells <strong>in</strong>side the uncerta<strong>in</strong>ty regionare searched <strong>in</strong> an order which depends on the amount of a priori <strong>in</strong>formation aboutthe correct code phase. If there is no a priori <strong>in</strong>formation, then the simplest straightl<strong>in</strong>eserial search scheme is typically utilized. If there is some a priori <strong>in</strong>formation,then more sophisticated search strategies, like Z-search or expand<strong>in</strong>g w<strong>in</strong>dow, couldbe used to improve synchronization performance (Polydoros 1982, Polydoros & Weber1984a, Polydoros & Weber 1984b). The serial search <strong>acquisition</strong> scheme was orig<strong>in</strong>allypresented <strong>in</strong> (Sage 1964). Hybrid serial-parallel search schemes have been proposed <strong>in</strong>(Milste<strong>in</strong> et al. 1985, Zhuang 1996, Baum & Veeravalli 1994).2.2.2 Detector structuresThe detector is another very important element <strong>in</strong> the <strong>acquisition</strong> circuit. The searchstrategy tells which cell will be <strong>in</strong>vestigated next and the detector makes a decisionwhether this cell corresponds to the synchro position or not. The detector can be dividedfurther <strong>in</strong>to two operational blocks: decision variable calculation and thresholdcomparison, as depicted <strong>in</strong> the block diagram of Fig. 2.27
x(t)Decisionvariablecalculationz(τ i )ThresholdcomparisonDecision (ˆτ)T HAdjustablelocal codegeneratorThresholdsett<strong>in</strong>gDetectorFig 2. Detector structure.Decision variable calculationIn the decision variable calculation process, such a decision variable z(τ i ) is determ<strong>in</strong>edfrom which the decision about synchro or nonsynchro position can be made as reliablyas possible. It has been shown <strong>in</strong> (Polydoros 1982) that <strong>in</strong> the AWGN channel, and withoutdata modulation, the optimal method to calculate a decision variable is to correlatethe received signal x(t) with the local reference signal asz(τ i )=∫ Td0x(t)c(t − τ i )cos(2π f c t + 2π f d t + φ) dt, (3)where τ i is a delay cell which is presently under <strong>in</strong>vestigation, and T d is known asthe <strong>in</strong>tegration time, dwell time or observation time. Dwell time can be fixed or variable,where the latter is also known as sequential detection (Polydoros 1982). Most ofthe studies deal<strong>in</strong>g with code <strong>acquisition</strong> are based on fixed-dwell time detectors, butsome approaches based on variable-dwell time detectors are studied <strong>in</strong> (Gumacos 1963,Davidovici et al. 1984, Su & Weber 1990, Ravi & Ormondroyd 1991, Wang & Sheen2000, L<strong>in</strong> 2002). It can be observed from (3) that f c , f d and φ must be known beforecorrelation. This is known as coherent detection. Usually, coherent detection is notused, because estimation of the carrier phase is difficult from the DS/SS-signal whichhas a low signal-to-noise ratio (SNR). However, it has been studied <strong>in</strong> some references(Davidovici et al. 1984, Jianl<strong>in</strong> & Tantaratana 1995, Madhow & Pursley 1995, Salih28
& Tantaratana 1996, I<strong>in</strong>atti 1997, Salih & Tantaratana 1999, Delva & Howitt 2001)due to simplified analysis. Therefore, code synchronization is normally carried out beforecarrier synchronization us<strong>in</strong>g noncoherent detection. In the noncoherent receiver,a decision variable is calculated either via an envelope detector√z(τ i )= z 2 I (τ i)+z 2 Q (τ i) or a square-law detector z(τ i )=z 2 I (τ i )+z 2 Q(τ i ), (4)where the <strong>in</strong>-phase (z I ) and quadrature (z Q ) correlations are def<strong>in</strong>ed as (Polydoros 1982)√ ∫ 2 Tdz I (τ i )= x(t)c(t − τ i )cos(2π f c t + 2π f d t) dt (5)T d 0and√ ∫ 2 Tdz Q (τ i )= x(t)c(t − τ i )s<strong>in</strong>(2π f c t + 2π f d t) dt. (6)T d 0The third type is the differentially coherent (DC) detector structure, which is studied <strong>in</strong>(Chung 1995, Zarrabizadeh & Sousa 1997, I<strong>in</strong>atti & Pouttu 1999). Decision variable<strong>in</strong> the DC detector is obta<strong>in</strong>ed by multiply<strong>in</strong>g the present and delayed samples amongthemselves. It has been shown <strong>in</strong> (Zarrabizadeh & Sousa 1997) that the DC detectionoffers about 4-5 dB improvement <strong>in</strong> performance compared to the noncoherent detection.Coherent detection has the best performance, but unfortunately it cannot be used<strong>in</strong> practice.The correlation between the received and local codes can be performed us<strong>in</strong>g an activeor passive correlation method depicted <strong>in</strong> Fig. 3. In the active correlation method,the conventional correlator structure is used. There<strong>in</strong>, the received and local codes aremultiplied on a chip-by-chip basis and then <strong>in</strong>tegrated over T d . Therefore, decision variablesare generated at a rate of 1/T d . The term active refers to the fact that a code phaseunder <strong>in</strong>vestigation must be actively set dur<strong>in</strong>g the code <strong>acquisition</strong> process. The secondalternative is to use a passive correlat<strong>in</strong>g unit, where correlation is performed us<strong>in</strong>gthe code matched filter <strong>in</strong>stead of the correlator. Therefore, this method is known asMF <strong>acquisition</strong>, where the impulse response of the MF is time-reversed and a delayedversion of the <strong>spread</strong><strong>in</strong>g code <strong>in</strong> some predeterm<strong>in</strong>ed code phase. With these sett<strong>in</strong>gs,a MF performs exactly the same correlation operation as was calculated <strong>in</strong> active correlation.The only difference is the rate at which decision variables are generated. In MF<strong>acquisition</strong>, <strong>in</strong>tegration time is typically the whole <strong>spread</strong><strong>in</strong>g code, i.e., over L chips.Chips are correlated simultaneously <strong>in</strong> MF operation and, thus, decision variables fromMF output are achieved at rate 1/T c . So, if the <strong>in</strong>tegration time is the same <strong>in</strong> both cases,then passive MF <strong>acquisition</strong> produces decision variables L times as fast as <strong>in</strong> the active29
correlator. The term passive refers to the fact that the receiver waits until the receivedcode phase matches with a predeterm<strong>in</strong>ed code phase. The active correlator and passiveMF approaches are studied <strong>in</strong> more detail <strong>in</strong> (Polydoros & Weber 1984a, Polydoros &Weber 1984b, Rappaport & Grieco 1984). The output signal of an active or passive correlationunit is proportional to the autocorrelation function of the <strong>spread</strong><strong>in</strong>g code. S<strong>in</strong>cethe used <strong>spread</strong><strong>in</strong>g codes have been especially designed to have good autocorrelationproperties, <strong>in</strong> an AWGN channel there occurs a clear spike at a time <strong>in</strong>stant when thereceived and local codes are synchronized.a) Active correlat<strong>in</strong>g unit(correlator)b) Passive correlat<strong>in</strong>g unit(matched filter)x(t)T d∫ ( ⋅ ) dt0z(τ i)x(t) MFz(τ i)h(t)=c(τ i-t)c(t-τ i)Fig 3. Active and passive correlation units (Katz 2002).Threshold comparisonIn threshold comparison, a decision variable z(τ i ) is compared to a threshold T H (Fig.2) <strong>in</strong> order to make a decision about a cell which is presently under <strong>in</strong>vestigation. Thethreshold is set above the noise level but below the ma<strong>in</strong> peak of the autocorrelationfunction. If the threshold is exceeded, then the detector makes a decision that a synchrocell has been found and synchronization process enters to the code track<strong>in</strong>g mode. Ifthe threshold is not exceeded, then the detector decides that a nonsynchro cell has beenfound and <strong>acquisition</strong> process will cont<strong>in</strong>ue to a next code phase, which is determ<strong>in</strong>edby the search strategy. A decision made by a detector can be either correct or wrongbecause z(τ i ) is a random variable. If an actual synchro cell is under <strong>in</strong>vestigation, thenit is detected correctly (i.e., the threshold is exceeded) with probability of detection P Dor missed (i.e., the threshold is not exceeded) with probability of miss P miss = 1 − P D .Correspond<strong>in</strong>gly, if a nonsynchro cell is under <strong>in</strong>vestigation, then it is wrongly detected(i.e., a threshold is exceeded) with a probability of false alarm P FA and correctly (i.e., athreshold is not exceeded) with probability 1 − P FA .30
The threshold sett<strong>in</strong>g is a critical phase <strong>in</strong> a detector, because it affects P D , P FA and,thus, to the overall performance of the <strong>acquisition</strong> system. The threshold value can bebased on the Neyman-Pearson decision criterion if all the cells have the same thresholdvalue (Polydoros 1982). This criterion leads to a detector, which has a fixed P FA <strong>in</strong> eachcell. When a priori <strong>in</strong>formation on the synchro cell is available, the Bayesian type ofdetector can be used where the threshold can be different for each cell (Polydoros 1982).Then, lower thresholds are used for probably correct cells. However, some parameterslike noise variance are typically unknown <strong>in</strong> the receiver. If estimated parameter values<strong>in</strong>stead of actual ones are used <strong>in</strong> detection, then the detector is called as a generalizedlikelihood ratio test (GLRT) or a constant false alarm rate (CFAR) detector (Kay 1998).Fixed thresholds can be used <strong>in</strong> stable channel conditions, but <strong>in</strong> dynamic channelconditions, where <strong>in</strong>terference is present, adaptive threshold schemes are used. S<strong>in</strong>cethe threshold should be set above the noise level, the detector has to estimate noisepower that prevails just before threshold comparator. After noise power estimation,the threshold value can be solved theoretically from the probability density function(PDF) of the signal com<strong>in</strong>g <strong>in</strong>to the comparator, as <strong>in</strong> (Rappaport 1969), to obta<strong>in</strong> aCFAR threshold. There are also threshold sett<strong>in</strong>g schemes where SNR or <strong>in</strong>terferenceto-signalratio (I/S) estimators are used to control the threshold (Siess & Weber 1986).Instead of estimat<strong>in</strong>g noise power, SNR or I/S, methods exploit<strong>in</strong>g a reference filterfor threshold sett<strong>in</strong>g are also <strong>in</strong>vestigated. In those methods, an impulse response ofthe reference filter is orthogonal to the orig<strong>in</strong>al PN <strong>sequence</strong> (Ziemer & Peterson 1985,Ibrahim & Aghvami 1994). An adaptive threshold sett<strong>in</strong>g based on a calculated numberof cross<strong>in</strong>gs dur<strong>in</strong>g a given amount of code periods is proposed <strong>in</strong> (Glisic 1988a). Afterthat, the threshold can be adjusted by a small <strong>in</strong>crement toward the target cross<strong>in</strong>g-rate.Instantaneous threshold sett<strong>in</strong>g and CFAR algorithms are analyzed <strong>in</strong> (Glisic 1988b).A rank filter based threshold sett<strong>in</strong>g is considered <strong>in</strong> (I<strong>in</strong>atti 1996), and a median filterbased scheme <strong>in</strong> (Stojanovic & Jovanovic 1992). Wide comparisons between the differentthreshold sett<strong>in</strong>g methods and the maximum-selection method is studied <strong>in</strong> (I<strong>in</strong>atti2000a). An adaptive threshold sett<strong>in</strong>g, which is based on the output power estimationof an adaptive antenna array is considered <strong>in</strong> (Wang & Kwon 2000b). It is shown <strong>in</strong>(Lee & Kim 2001, Lee & Kim 2002) that the use of multiple thresholds improves the<strong>acquisition</strong> performance compared to the s<strong>in</strong>gle threshold approaches.The methods considered so far make a cell decision based on a s<strong>in</strong>gle-dwell. Thisis not effective from the <strong>acquisition</strong> time po<strong>in</strong>t of view, because the same dwell-timeis spent on test<strong>in</strong>g the nonsynchro cells than the synchro cell. Therefore, multiple-31
dwell approaches have been developed to reduce the <strong>acquisition</strong> time (DiCarlo & Weber1983). In multiple-dwell <strong>acquisition</strong>, the first test level is followed by one or moreobservations of the same cell, if the first threshold is exceeded. This allows the check<strong>in</strong>gof nonsynchro cells rapidly, because a shorter <strong>in</strong>tegration time is used <strong>in</strong> the first test.If the first threshold is exceeded, then the correctness of the first decision is verifiedby equal length or longer observation, thus avoid<strong>in</strong>g false alarms. The synchro cellis declared only after all the stages <strong>in</strong>dicate a synchro cell position. Multiple-dwell<strong>acquisition</strong> is classified as the immediate rejection scheme if the <strong>acquisition</strong> processimmediately moves on to the next cell only after one failure <strong>in</strong> synchro cell detectionat any dwell stages. There are also more advanced multiple-dwell approaches, like thetwo-step rejection and the up-down counter method, where past behavior of the cellis taken <strong>in</strong>to account before reject<strong>in</strong>g it (Simsa & Triska 1994, Triska & Simsa 1994,Eynon & Tozer 1995).2.3 Performance measuresThe ultimate task of the code <strong>acquisition</strong> subsystem is to coarsely phase-align the receivedand local <strong>spread</strong><strong>in</strong>g codes. Success of this task can be measured <strong>in</strong> many differentways, as can be seen from the wide literature on the topic. This section willgive an overview of different performance measures used <strong>in</strong> code <strong>acquisition</strong>. First ofall, a code track<strong>in</strong>g loop requires that the error between received and local PN codesdoes not exceed half of a chip duration after the code <strong>acquisition</strong> process has concluded.Therefore, one alternative is to perform a delay estimation for the received signal andthen evaluate an amount of error of this estimate. Accord<strong>in</strong>g to Polydoros (1982) <strong>in</strong> anoncoherent receiver, the maximum likelihood (ML) method for delay estimation <strong>in</strong> aAWGN channel is the structure shown <strong>in</strong> Fig. 4.MF|·|selectmaximumˆτ MLFig 4. ML receiver for delay estimation <strong>in</strong> AWGN channel.More precisely, the ML receiver correlates the received signal with the <strong>spread</strong><strong>in</strong>g code,then performs envelope detection and f<strong>in</strong>ally makes such a decision that the ML delay32
estimate ˆτ ML is a time <strong>in</strong>stant which corresponds to the maximum of the envelope detectoroutput with<strong>in</strong> a given time frame. The ML estimate is def<strong>in</strong>ed as the value of theunknown parameter for which the likelihood function is maximized. RMSE of delayestimate is an often used performance measure <strong>in</strong> delay estimation literature and it canbe calculated <strong>in</strong> chips as√√( ˆτML) ( ˆτML) { τ − ˆτML} 2.RMSE = MSE = E(7)T c T c T cIf the RMSE of delay estimate is smaller than half of a chip time, it can be concludedthat code <strong>acquisition</strong> succeeds, but it does not tell that how much time this takes. Indelay estimation, we either have to take multiple samples from one chip or use <strong>in</strong>terpolation<strong>in</strong> order to get more accurate estimates. In an extreme case, the receiver shouldhave an <strong>in</strong>f<strong>in</strong>ite number of values with<strong>in</strong> the uncerta<strong>in</strong>ty region and perform the impossibletask of correlat<strong>in</strong>g the received data with the <strong>in</strong>f<strong>in</strong>ite number of code shiftsand choose the maximum. Therefore, the uncerta<strong>in</strong>ty region is quantized <strong>in</strong>to smallfragments or "cells" <strong>in</strong> order to simplify the implementation of the receiver. Thus, theestimation problem is transformed <strong>in</strong>to a multiple-hypothesis test<strong>in</strong>g problem (Polydoros1982). Therefore, the papers which are deal<strong>in</strong>g with estimation are <strong>in</strong>terestedabout the question of how well a parameter can be estimated under an assumption thatthe uncerta<strong>in</strong>ty region is not divided <strong>in</strong>to cells and all decision variables can be calculatedsimultaneously. They are not deal<strong>in</strong>g with the question of how much time it takesto f<strong>in</strong>d the parameter.When a suboptimal but implementable receiver structure is used, where the uncerta<strong>in</strong>tyregion is quantized <strong>in</strong>to small cells, then new performance measures are <strong>in</strong>troduced.The goal of the code <strong>acquisition</strong> unit is now to decide which one of all thepossible discrete code phases is with<strong>in</strong> a cell’s distance from the phase of the receivedcode. Therefore, synchronization probabilities (P D , P FA and P miss ) can be consideredas performance measures. Most often <strong>in</strong> code <strong>acquisition</strong> literature we are <strong>in</strong>terestedabout the total <strong>acquisition</strong> time T ACQ which is consumed dur<strong>in</strong>g the <strong>acquisition</strong> process.This is a widely used performance measure, because it <strong>in</strong>cludes the effects of multipledesign parameters (threshold sett<strong>in</strong>g, correlation time, number of cells, search strategy,verification, code length, penalty time of false alarm, SNR, etc.). When T ACQ is usedas a performance measure, then two basic scenarios can be dist<strong>in</strong>guished based on theexistence of the stopp<strong>in</strong>g time T stop (Polydoros 1982). If the radio l<strong>in</strong>k operates cont<strong>in</strong>uously,then the time limit T stop does not exist, which def<strong>in</strong>es when the synchronization33
must be f<strong>in</strong>ished. In this case the most important parameter is the mean <strong>acquisition</strong> timeT MA = E{T ACQ } and sometimes also the variance of <strong>acquisition</strong> time σT 2 ACQ(Polydoros1982). The mean <strong>acquisition</strong> time should be as short as possible. In burst or packet likecommunications, <strong>in</strong>itial synchronization should take place before data reception starts,i.e., before the time limit T stop . Then the better performance measure is the probabilityof <strong>acquisition</strong> P ACQ = P{T ACQ ≤ T stop }, which should be as high as possible. P ACQ isalso called the overall probability of detection P ovDand its complement as the overallprobability of miss<strong>in</strong>g the code Pmiss ov = 1 − Pov D (Polydoros 1982).Because the <strong>acquisition</strong> time is a random variable, it has PDF f ACQ (t) which can beused <strong>in</strong> derivation of aforementioned performance measures. This method is also knownas the <strong>direct</strong> approach and it is based on the algebraic characterization of the searchstrategy (DiCarlo & Weber 1980, Braun 1982, We<strong>in</strong>berg 1983, Meyr & Polzer 1983,Jovanovic 1988, Jovanovic 1992, Corazza 1996, Pan et al. 1995, Simsa 1996). However,a closed-form expression for f ACQ (t) is often difficult to obta<strong>in</strong>. Performance measuresT MA , σT 2 ACQand P ACQ can also be obta<strong>in</strong>ed without knowledge of the f ACQ (t) us<strong>in</strong>g signalflow graph techniques <strong>in</strong> the transform doma<strong>in</strong> (Holmes & Chen 1977, Polydoros 1982,DiCarlo & Weber 1983, Polydoros 1984). Therefore, the former method is known asthe time doma<strong>in</strong> technique and the latter as the transform doma<strong>in</strong> technique.Because the T MA is the total time expected to be consumed from the start to the endof the <strong>acquisition</strong> process, the two most important issues which affect it are the searchstrategy and the detector structure which were discussed <strong>in</strong> Section 2.2. The detectorbr<strong>in</strong>gs along a set of parameters T H , P D , P FA , T d , and the false alarm penalty time T FA ,which all have an impact on the T MA .2.4 Performance results <strong>in</strong> various environmentsA review of code <strong>acquisition</strong> performance results <strong>in</strong> various environments are presented<strong>in</strong> this section. Most of the code <strong>acquisition</strong> literature considers the situation where onlyAWGN is disturb<strong>in</strong>g reception (e.g., Holmes & Chen 1977, Polydoros & Weber 1984a,Polydoros & Weber 1984b). In addition to AWGN, there may be also other sources ofdisturbance which are considered separately <strong>in</strong> the follow<strong>in</strong>g subsections.34
2.4.1 Fad<strong>in</strong>g channelsWhen a signal is transmitted through a radio channel, the received signal consists ofmultiple copies of the transmitted signal due to multipath propagation. The impulseresponse of the radio channel is time-variant, because the propagation delay, attenuationfactor and phase shift of each path are chang<strong>in</strong>g with time. Therefore, the impulseresponse of the typical radio channel fluctuates and it is modeled as a stochastic processc(ζ ;t), where ζ is the propagation delay, and t denotes that impulse response ischang<strong>in</strong>g with time. In a non-l<strong>in</strong>e-of-sight (NLOS) environment, c(ζ ;t) is modeledas a zero-mean complex valued Gaussian process and its envelope |c(ζ ;t)| is Rayleighdistributed. Such a channel is said to be a Rayleigh fad<strong>in</strong>g channel. If there is a l<strong>in</strong>e-ofsight(LOS) component, then |c(ζ ;t)| is Ricean distributed and the channel is said to bea Ricean fad<strong>in</strong>g channel. (Proakis 1995)Coherence bandwidth (B coh ) of the channel is <strong>in</strong>versely proportional to the multipath<strong>spread</strong>, so it is related to the <strong>spread</strong> of time parameter ζ . If the bandwidth of thetransmitted signal (W ≈ 1/T c ) is much smaller than B coh , then all of the frequency components<strong>in</strong> the transmitted signal undergo the same attenuation and phase shift, and thechannel is called frequency-nonselective or flat fad<strong>in</strong>g. In this case, the multipath components<strong>in</strong> the received signal are not resolvable. If the multipath <strong>spread</strong> of the channelis greater than the chip length of the <strong>spread</strong><strong>in</strong>g code (i.e., W ≫ B coh ), the multipathcomponents can be resolved <strong>in</strong>to a discrete number of paths and the channel is calledfrequency-selective s<strong>in</strong>ce frequency response is not flat with<strong>in</strong> the transmitted signal’sbandwidth. The bandwidth of a DS/SS signal is typically designed to be larger thanB coh , because it allows frequency diversity to be utilized via the Rake receiver. This isone advantage of <strong>spread</strong> <strong>spectrum</strong> <strong>systems</strong>. (Proakis 1995)Another way of characteriz<strong>in</strong>g a radio channel is by the time variation t <strong>in</strong> c(ζ ;t).Coherence time (T coh ) is <strong>in</strong>versely proportional to the Doppler <strong>spread</strong>. When a transmitter,a receiver, or a reflector <strong>in</strong> the environment is mov<strong>in</strong>g, then the received frequencyis different than the transmitted frequency and the amount of this frequency differenceis called the Doppler shift. S<strong>in</strong>ce each multipath component has its own Doppler shift,the <strong>spread</strong> of these Doppler shifts is known as the Doppler <strong>spread</strong>. The radio channelcan be classified as slow-fad<strong>in</strong>g if the symbol time (T s ) is much smaller than T coh .Inthis case, the channel rema<strong>in</strong>s fixed dur<strong>in</strong>g the symbol <strong>in</strong>terval. If T s ≫ T coh , then thechannel is called fast-fad<strong>in</strong>g. (Proakis 1995)35
Typically, fad<strong>in</strong>g channels are categorized accord<strong>in</strong>g to their fad<strong>in</strong>g rate by compar<strong>in</strong>gthe channel coherence time to the duration of a symbol, as was described above.However, from the code <strong>acquisition</strong> po<strong>in</strong>t of view, it is more reasonable to compare T cohaga<strong>in</strong>st the duration of the <strong>acquisition</strong> process itself. Therefore, <strong>in</strong> the slow fad<strong>in</strong>g channelsT coh ≫ T MA_max , where T MA_max is the maximum expected <strong>acquisition</strong> time. Thiscondition means that the channel rema<strong>in</strong>s unchanged dur<strong>in</strong>g the entire code <strong>acquisition</strong>process. When a new code <strong>acquisition</strong> process starts, there is also a new realizationof the channel. In the fast fad<strong>in</strong>g channels T coh ≪ T MA_m<strong>in</strong> , where T MA_m<strong>in</strong> is the m<strong>in</strong>imumexpected <strong>acquisition</strong> time. The above condition means that the channel will passthrough all the states every time the cell is searched. (Katz et al. 2002b)<strong>Code</strong> <strong>acquisition</strong> <strong>in</strong> a frequency-nonselective Rayleigh fad<strong>in</strong>g channel is studied <strong>in</strong>(Sourour & Gupta 1990), where the receiver <strong>in</strong>cludes parallel MFs. It is found thatfrequency-nonselective Rayleigh fad<strong>in</strong>g causes a severe degradation <strong>in</strong> <strong>acquisition</strong> performance.That is because the fad<strong>in</strong>g channel causes amplitude fluctuations, whichdeteriorates P D performance. Another result is that <strong>in</strong> the nonfad<strong>in</strong>g channel it is advantageousto <strong>in</strong>crease the MF length, i.e., decrease the number of parallel MF’s, while <strong>in</strong>the fad<strong>in</strong>g channel, the opposite is true. Thus, the parallelism improves performancemore <strong>in</strong> a fad<strong>in</strong>g channel than <strong>in</strong> a static channel if compared to the serial search approach.Acquisition performance <strong>in</strong> frequency-nonselective and frequency-selectiveRicean fad<strong>in</strong>g channels is studied <strong>in</strong> (Sourour & Gupta 1992), where the parallel MF <strong>acquisition</strong>scheme is <strong>in</strong>vestigated. It is concluded that frequency-selective fad<strong>in</strong>g is caus<strong>in</strong>gmore degradation to the <strong>acquisition</strong> time performance than the nonselective channel.The degradation <strong>in</strong>creases when the specular component becomes weaker. Analysis ofMF code <strong>acquisition</strong> with a reference filter <strong>in</strong> frequency-nonselective and frequencyselectiveRayleigh fad<strong>in</strong>g channels is presented <strong>in</strong> (Ibrahim & Aghvami 1994). Itis aga<strong>in</strong> concluded that frequency-selective fad<strong>in</strong>g causes more degradation <strong>in</strong> performancecompared to the frequency-nonselective channels. <strong>Code</strong> <strong>acquisition</strong> <strong>in</strong> afrequency-selective Rayleigh fad<strong>in</strong>g channel us<strong>in</strong>g a least mean square (LMS) adaptivefilter is studied <strong>in</strong> (El-Tarhuni & Sheikh 1998b). There<strong>in</strong>, both slow- and fast-fad<strong>in</strong>gcases are <strong>in</strong>vestigated. The results show that flat fad<strong>in</strong>g degrades the performance byabout 3 dB while frequency selective fad<strong>in</strong>g has only a 0.5 - 2 dB degradation, comparedto the AWGN case. The degradation us<strong>in</strong>g this adaptive filter structure is muchless than which was reported <strong>in</strong> (Sourour & Gupta 1990), where parallel MFs wereutilized. Thus, the paper presented by (El-Tarhuni & Sheikh 1998b) concludes thatfrequency selective fad<strong>in</strong>g produces less degradation than flat fad<strong>in</strong>g s<strong>in</strong>ce the system36
can exploit the <strong>in</strong>herent multipath diversity when acquir<strong>in</strong>g the strongest available path.This is a different result than that which was presented before.A differentially coherent detection technique for <strong>acquisition</strong> <strong>in</strong> a frequency-nonselectivefast Rayleigh fad<strong>in</strong>g channel is proposed <strong>in</strong> (Chung 1995). It outperforms the conventionalnoncoherent method. In (Tantaratana et al. 1995), a noncoherent sequential<strong>acquisition</strong> is <strong>in</strong>vestigated for AWGN and slowly Rayleigh fad<strong>in</strong>g channels. Results<strong>in</strong>dicate that fad<strong>in</strong>g does not affect the false alarm probabilities, but it can drasticallyreduce the probability of detect<strong>in</strong>g the alignment of the two PN <strong>sequence</strong>s. Optimaldecision strategies for code <strong>acquisition</strong> <strong>in</strong> frequency-selective fad<strong>in</strong>g channels are analyzed<strong>in</strong> (Rick & Milste<strong>in</strong> 1998) when full parallel search is utilized. <strong>Code</strong> <strong>acquisition</strong> isstudied <strong>in</strong> static multipath and fad<strong>in</strong>g multipath channels <strong>in</strong> (I<strong>in</strong>atti 2000b), where equationsfor the mean <strong>acquisition</strong> time <strong>in</strong> the multipath environment are derived. Results<strong>in</strong>dicate that the T MA performance <strong>in</strong> the static channel is better than <strong>in</strong> the slow-fad<strong>in</strong>gchannel. On the other hand, the T MA performance <strong>in</strong> the static channel is worse than <strong>in</strong>the fast-fad<strong>in</strong>g channel. <strong>Code</strong> <strong>acquisition</strong> based on multiple-dwell or sequential l<strong>in</strong>eartests are <strong>in</strong>vestigated for Rayleigh fad<strong>in</strong>g channels <strong>in</strong> (Wang & Sheen 2000, Lee & Kim2002). Novel methods to analyze code <strong>acquisition</strong> over correlated fad<strong>in</strong>g channels areproposed <strong>in</strong> (Sheen & Wang 2001, Corazza et al. 2004, Ca<strong>in</strong>i et al. 2004), where thechannel memory <strong>in</strong>curred by fad<strong>in</strong>g between different cell detections is taken <strong>in</strong>to account.An <strong>in</strong>terest<strong>in</strong>g proposal for a serial-search strategy exploit<strong>in</strong>g the possible knowledgeof the multipath delay <strong>spread</strong> is reported <strong>in</strong> (Sh<strong>in</strong> & Lee 2001), where consecutivetests are performed on cells which are separated by the number of resolvable paths.The proposed <strong>acquisition</strong> scheme significantly outperforms the conventional schemesover frequency-selective Rayleigh fad<strong>in</strong>g channels. The method where the energy ofall the multipath components are utilized already at the <strong>acquisition</strong> stage is proposed <strong>in</strong>(I<strong>in</strong>atti & Latva-aho 1998, I<strong>in</strong>atti & Latva-aho 2001). Acquisition with a Rake receiveris studied <strong>in</strong> (Garrett & Noneaker 1998, Glisic & Katz 2001). Delay estimation <strong>in</strong> a<strong>direct</strong> <strong>sequence</strong> code division multiple access (DS/CDMA) system operat<strong>in</strong>g over fad<strong>in</strong>gchannels is studied e.g., <strong>in</strong> (Ström & Parkvall 1995, Ström et al. 1996a, Ström &Malmsten 1998).37
2.4.2 Data modulationAcquisition of the DS/SS signal is often accomplished <strong>in</strong> the absence of data modulationto speed up the <strong>acquisition</strong> process. However, there are cases when it becomes necessaryto acquire the <strong>in</strong>com<strong>in</strong>g code <strong>in</strong> the presence of data modulation, e.g., re-<strong>acquisition</strong>. Itis shown <strong>in</strong> (Siess & Weber 1986) that the detection probability of an active noncoherentI-Q detector degrades considerably when the DS/SS is b<strong>in</strong>ary phase shift key<strong>in</strong>g (BPSK)modulated. This degradation can be partially overcome by us<strong>in</strong>g multiple receivers tomake the decision such that each receiver is tuned to expect a data bit transition <strong>in</strong> adifferent position with<strong>in</strong> the correlation period. In so do<strong>in</strong>g, one is able to atta<strong>in</strong> a highP D <strong>in</strong> at least one of the receivers. Another approach to avoid data degradation is todivide the <strong>in</strong>tegration <strong>in</strong>terval <strong>in</strong>to several sub<strong>in</strong>tervals which are then noncoherentlycomb<strong>in</strong>ed for decision mak<strong>in</strong>g (Cheng 1988, Cheng et al. 1990, Davisson & Flikkema1988).The optimal ML code <strong>acquisition</strong> scheme with data modulation is presented <strong>in</strong> (Li& Tantaratana 1995). However, the ML scheme is not practical for implementation, sothe authors presented several suboptimal schemes. The results show that these suboptimalschemes have almost the same performance as conventional schemes when thereis no data modulation and significantly better <strong>in</strong> the presence of data modulation. Correspond<strong>in</strong>gly,optimal and suboptimal noncoherent code <strong>acquisition</strong> methods with datamodulation are considered <strong>in</strong> (Su et al. 1998, Kwon & Tarafder 1996). Maximum likelihoodcode <strong>acquisition</strong> <strong>in</strong> the presence of multipath propagation, Doppler shift anddata modulation is considered <strong>in</strong> (Wetzker et al. 1998). Differentially coherent code<strong>acquisition</strong> <strong>in</strong> the presence of data modulation is studied <strong>in</strong> (I<strong>in</strong>atti & Pouttu 1999) anddifferentially noncoherent <strong>in</strong> (Pirhonen & Ristaniemi 2002). The results show that differentiallycoherent scheme with data removal unit offers about 4 dB improvement <strong>in</strong>performance compared to the noncoherent <strong>acquisition</strong>. A fast code <strong>acquisition</strong> schemebased on fast Fourier transform (FFT) and noncoherent <strong>in</strong>tegration is proposed <strong>in</strong> (Chenet al. 2007).2.4.3 Doppler effectThe Doppler effect is caused by the relative movement between transmitter and receiver.It shifts the received carrier frequency and code rate, so the Doppler effect can be divided<strong>in</strong>to the carrier Doppler and code Doppler, respectively. The carrier Doppler38
causes a significant reduction of the correlation peak and SNR (Polydoros 1982, Mausset al. 1993). The code Doppler has to be taken <strong>in</strong>to account only if the carrier Doppleris large (Cheng et al. 1990), and hence the carrier Doppler is the most commonly <strong>in</strong>vestigatedDoppler effect <strong>in</strong> code <strong>acquisition</strong> literature. If the code Doppler can beconsidered as <strong>in</strong>significant, then we have a two-dimensional (time – frequency) uncerta<strong>in</strong>tyregion from which the correct delay and carrier frequency must be found (Holmes& Chen 1977, Peterson et al. 1995). Two-dimensional search can be performed bychang<strong>in</strong>g both the center frequency and code phase of the receiver. However, the actualcarrier Doppler is usually not an <strong>in</strong>teger multiple of the cell spac<strong>in</strong>g, a fractional carrierDoppler still rema<strong>in</strong>s after the search<strong>in</strong>g process is completed. The article (Yoon et al.2008) <strong>in</strong>troduces a new method which can be used for fight<strong>in</strong>g aga<strong>in</strong>st fractional carrierDoppler.If the code Doppler cannot be considered as <strong>in</strong>significant, then time vary<strong>in</strong>g tim<strong>in</strong>goffset causes problems and a two-dimensional time-frequency search cannot be <strong>direct</strong>lyapplied. A highly parallel scheme is proposed <strong>in</strong> (Cheng et al. 1990), where the localreference signal of each parallel unit is adjusted such that the correspond<strong>in</strong>g carrier andthe code Doppler offsets are compensated. This requires a large number of parallelunits, but it caused only 3 dB SNR loss compared to the case when there are no carrierand code Doppler offsets. The code Doppler problem is <strong>in</strong>vestigated also <strong>in</strong> (Fuxjaeger& Iltis 1994), where a short duration correlation is used to acquire tim<strong>in</strong>g <strong>in</strong>formationand a subsequent longer correlation to acquire the Doppler velocity. <strong>Code</strong> Dopplerhas an effect on the probability of detection and on the search rate of the <strong>acquisition</strong>process, and thus on the mean <strong>acquisition</strong> time (Holmes & Chen 1977, Simon et al.1994, Bezueha 2002). The code <strong>acquisition</strong> <strong>in</strong> the presence of both carrier and codeDoppler is <strong>in</strong>vestigated <strong>in</strong> (Su et al. 1995).2.4.4 Intentional <strong>in</strong>terferenceThe receiver may encounter also <strong>in</strong>tentional <strong>in</strong>terfer<strong>in</strong>g signals, which are especiallydesigned to h<strong>in</strong>der the receiver’s operation. These signals are also known as jamm<strong>in</strong>gsignals. In order to ensure successful synchronization and data detection under <strong>in</strong>terference,it has been necessary to develop different types of <strong>in</strong>terference cancellers. Ins<strong>in</strong>gle antenna <strong>systems</strong>, <strong>in</strong>terference cancellation methods are based on either a time orfrequency doma<strong>in</strong> analyz<strong>in</strong>g and process<strong>in</strong>g of signals. An overview of these methods ispresented <strong>in</strong> (Milste<strong>in</strong> 1988a). Examples of frequency doma<strong>in</strong> <strong>in</strong>terference cancellers39
are the notch filter (Davidovici & Kanterakis 1989), the phase <strong>in</strong>terference extractor(Kanterakis 1994), the consecutive mean excision (CME) (Henttu & Aromaa 2002),and the forward consecutive mean excision (FCME) (Saarnisaari & Henttu 2003) algorithms.Time doma<strong>in</strong> <strong>in</strong>terference cancellation can be realized us<strong>in</strong>g adaptive filter<strong>in</strong>g(Gardner 1993, Zhang et al. 1999), constant modulus algorithm (Mendoza et al. 1989),and s<strong>in</strong>gular value decomposition (Henttu 2000). The comb<strong>in</strong>ed time-frequency <strong>in</strong>terferencecancellation is studied <strong>in</strong> (Tazebay & Akansu 1995). Many of those methodsare capable of suppress<strong>in</strong>g only narrowband <strong>in</strong>terfer<strong>in</strong>g signals, but some wideband<strong>in</strong>terference cancellation methods are proposed <strong>in</strong> (Gardner 1993, Henttu 2000). Jamm<strong>in</strong>gsignals can also be suppressed us<strong>in</strong>g smart antennas, <strong>in</strong> which case <strong>in</strong>terferencecancellation is accomplished <strong>in</strong> a spatial doma<strong>in</strong>. This is one of the ma<strong>in</strong> topics of thisthesis, thus it is studied <strong>in</strong> more detail <strong>in</strong> Chapters 3 and 4.The effects of <strong>in</strong>terference on the code <strong>acquisition</strong> performance is studied <strong>in</strong> somepapers. It is shown <strong>in</strong> (Siess & Weber 1986) that the performance of <strong>in</strong>itial code <strong>acquisition</strong>is very sensitive to s<strong>in</strong>usoidal and pulsed <strong>in</strong>terference. Authors also concludedthat the s<strong>in</strong>usoidal <strong>in</strong>terference is an optimal <strong>in</strong>terference <strong>in</strong> some situations caus<strong>in</strong>gthe highest damage. S<strong>in</strong>usoidal <strong>in</strong>terference is also known as tone or cont<strong>in</strong>uous wave(CW) <strong>in</strong>terference. The study of s<strong>in</strong>usoidal <strong>in</strong>terference is extended to sequential detectors<strong>in</strong> (Ravi & Ormondroyd 1992). Interference suppression or rejection techniques arealso studied <strong>in</strong> context of code <strong>acquisition</strong>. A transform doma<strong>in</strong> notch filter is proposed<strong>in</strong> (Milste<strong>in</strong> 1988b) to suppress strong s<strong>in</strong>usoidal <strong>in</strong>terference. The notch filter was designedto null-out 10% of the <strong>spread</strong>-<strong>spectrum</strong> bandwidth. The results show that thenotch filter can significantly improve <strong>acquisition</strong> performance under s<strong>in</strong>usoidal <strong>in</strong>terference.The l<strong>in</strong>ear prediction filter as an <strong>in</strong>terference suppressor is studied <strong>in</strong> (Gottesman& Milste<strong>in</strong> 1990). The results <strong>in</strong>dicate that at small <strong>in</strong>terference bandwidths, the l<strong>in</strong>earprediction filter system outperforms the transform doma<strong>in</strong> notch filter system. On theother hand, at larger <strong>in</strong>terference bandwidths, the transform doma<strong>in</strong> notch filter systemoutperforms the l<strong>in</strong>ear prediction filter system. The strategy where the median filter isplaced after the matched filter is proposed <strong>in</strong> (I<strong>in</strong>atti 1997). There<strong>in</strong>, the effects of s<strong>in</strong>usoidaland pulsed <strong>in</strong>terference on code <strong>acquisition</strong> are <strong>in</strong>vestigated. The results showthat the comb<strong>in</strong>ation of matched filter and median filter is robust aga<strong>in</strong>st <strong>in</strong>vestigated<strong>in</strong>terference types, and its performance is better or equal than that of the transversalfilter approach.It can be concluded that s<strong>in</strong>usoidal <strong>in</strong>terference is not a good choice for jamm<strong>in</strong>g,s<strong>in</strong>ce its cancellation is relatively easy. Narrowband <strong>in</strong>terfer<strong>in</strong>g signals can also be40
cancelled us<strong>in</strong>g a phase <strong>in</strong>terference extractor (Saarnisaari 2000) and a whiten<strong>in</strong>g filter(Saarnisaari 2001). Performance of differentially coherent code <strong>acquisition</strong> under s<strong>in</strong>usoidal<strong>in</strong>terference is studied <strong>in</strong> (Ristaniemi & Pirhonen 2002). Differentially coherentand differentially noncoherent <strong>acquisition</strong> schemes under narrowband <strong>in</strong>terference areconsidered <strong>in</strong> (I<strong>in</strong>atti & Pouttu 1999) and the study is extended to cover also wideband<strong>in</strong>terference <strong>in</strong> (I<strong>in</strong>atti & Pouttu 2000). The results show that both schemes outperformconventional noncoherent MF code <strong>acquisition</strong>. In the wideband <strong>in</strong>terferencestudy, bandwidths of <strong>in</strong>terfer<strong>in</strong>g signals were 20% and 100% of the DS/SS signal bandwidth.The results <strong>in</strong>dicate that the differentially coherent approach is robust aga<strong>in</strong>stlow-powered wideband <strong>in</strong>terference.2.4.5 Multiple-access <strong>in</strong>terferenceThe other users of the same CDMA system cause so called multiple-access <strong>in</strong>terference(MAI) for each other, because the <strong>spread</strong><strong>in</strong>g codes of different users are not fully orthogonal.If there is a large number of users, then the central limit theorem can be applied tomodel the MAI as a Gaussian process, which <strong>in</strong>creases the total noise power (Corazza1996, Zhuang 1996). Noise enhancement naturally reduces P D and deteriorates T MA performance.It has been shown <strong>in</strong> (Madhow & Pursley 1993) that the <strong>acquisition</strong> basedcapacity of a CDMA system may be smaller than the bit error rate (BER) based capacity,if a coherent detector without verification is used. However, the verification modemakes the <strong>acquisition</strong> process more robust aga<strong>in</strong>st the MAI (Corazza & Degli-Esposti1994). Multiple-access <strong>in</strong>terference cannot be necessarily modeled as a Gaussian processif there are only a few users or they have different data rates. Then, the effectof MAI can be analyzed by mapp<strong>in</strong>g it to the search process as a source of disturb<strong>in</strong>gsignal (Katz & Glisic 1998, Katz & Glisic 2000).Different k<strong>in</strong>ds of <strong>acquisition</strong> structures have been proposed to be applied <strong>in</strong> thepresence of MAI. A two-stage hybrid <strong>acquisition</strong> method based on passive and activecorrelation can achieve rapid <strong>acquisition</strong> <strong>in</strong> the absence of a near-far problem (Madhow& Pursley 1995). If there is a near-far problem, it has a considerable degrad<strong>in</strong>g effecton the <strong>acquisition</strong> based capacity (Corazza & Degli-Esposti 1994). In that case such receiverstructures must be developed which take the MAI <strong>in</strong>to account. Typically thesestructures conta<strong>in</strong> a separate MAI cancellation unit just before an <strong>acquisition</strong> circuitlike <strong>in</strong> (Madhow 1997, El-Tarhuni & Sheikh 1998a, Smith & Miller 1999, Lee & Kim2001, Bharadwaj & Buehrer 2004). A code <strong>acquisition</strong> method where two cascaded41
matched filters are employed is proposed <strong>in</strong> (Kim et al. 2001). <strong>Code</strong> <strong>acquisition</strong> algorithmswhich utilize differential correlations are <strong>in</strong>vestigated for CDMA <strong>systems</strong> <strong>in</strong>(Ristaniemi & Joutsensalo 2001). It was shown that the receiver can efficiently filternoise and <strong>in</strong>terference when a constant preamble or an unmodulated pilot channel isavailable for the desired user. Pilot-aided synchronization is studied also <strong>in</strong> (Ristaniemi1999b, Ristaniemi 1999a). The propagation delay estimation problem under a near-fareffect is studied <strong>in</strong> (Ström et al. 1996b). The code <strong>acquisition</strong> performance of CDMA<strong>systems</strong> with antenna array is studied <strong>in</strong> (Kim 2004, Kim 2005). The results show thatantenna arrays significantly improve the <strong>acquisition</strong> performance over the case of a s<strong>in</strong>gleantenna. <strong>Code</strong> <strong>acquisition</strong> under severe MAI conditions with multiple antennas isstudied <strong>in</strong> (Reed 2004, Reed et al. 2008).2.4.6 Other system <strong>in</strong>terferenceOther communication <strong>systems</strong> which utilize the same frequency band can disturb thereception of DS/SS signals. This k<strong>in</strong>d of situation is denom<strong>in</strong>ated as a CDMA overlaysystem. In (Gottesman & Milste<strong>in</strong> 1996) a l<strong>in</strong>ear prediction filter is used to suppressnarrowband users. The results show that narrowband overlay <strong>in</strong>terference can be effectivelyelim<strong>in</strong>ated us<strong>in</strong>g the proposed scheme. When the bandwidth is larger, the performancedeteriorates due to a wider notch. The performance of a l<strong>in</strong>ear prediction filterstructure is also very dependent on the <strong>in</strong>terference power. An <strong>in</strong>terference suppressionfilter is employed also <strong>in</strong> (Kim 2001) to suppress narrowband overlay <strong>in</strong>terference.The performance of the subspace and MMSE based code <strong>acquisition</strong> techniques underoverlay <strong>in</strong>terference are compared <strong>in</strong> (Miller 1995). The results show that both of thesetechniques are robust towards narrowband <strong>in</strong>terference and near far cochannel <strong>in</strong>terference.<strong>Code</strong> <strong>acquisition</strong> for a DS/CDMA overlay system with imperfect power controlis analyzed <strong>in</strong> (Kim & Lee 1997). Generally, the same k<strong>in</strong>d of <strong>in</strong>terference cancellationmethods can be applied as <strong>in</strong> the case of <strong>in</strong>tentional <strong>in</strong>terference.2.4.7 Multiple antennasWhen multiple antennas are utilized, then two basic techniques can be dist<strong>in</strong>guished depend<strong>in</strong>gon the distance between antenna elements. If the antenna elements are widelyspaced (i.e., the distance between antenna elements is at least few wavelengths), then antennadiversity techniques can be exploited. If antenna elements are closely spaced (i.e.,42
the distance between antenna elements is at most half a wavelength), then beamform<strong>in</strong>gtechniques can be exploited.Widely spaced antenna elements ensure that signals <strong>in</strong> different antennas undergo<strong>in</strong>dependent fad<strong>in</strong>g processes. This property is used <strong>in</strong> antenna diversity techniques toimprove SNR <strong>in</strong> fad<strong>in</strong>g channels. However, the signals of different diversity branchescannot be comb<strong>in</strong>ed coherently dur<strong>in</strong>g <strong>in</strong>itial code <strong>acquisition</strong>, because channel coefficientsare unknown at this stage. Hence, noncoherent comb<strong>in</strong><strong>in</strong>g is typically utilized.Parallel code <strong>acquisition</strong> with antenna diversity is studied <strong>in</strong> (Rick & Milste<strong>in</strong> 1997)for both frequency-nonselective and frequency-selective fad<strong>in</strong>g channels. The authorsfound that antenna diversity substantially improves <strong>acquisition</strong> performance when morediversity branches are used. Two schemes for code <strong>acquisition</strong> us<strong>in</strong>g antenna array areproposed <strong>in</strong> (Park & Oh 1998). In the studied receiver, there is a correlator <strong>in</strong> eachantenna branch. In the first scheme, the output signals from correlators are not comb<strong>in</strong>ed,but <strong>acquisition</strong> is declared only if the multiple of those outputs signals exceedthe threshold. In the second scheme, the output signals from correlators are comb<strong>in</strong>ed.The results show that the <strong>acquisition</strong> performance of the proposed schemes becomes improvedcont<strong>in</strong>ually as the number of antennas <strong>in</strong>creases. This study is extended to coveralso parallel search <strong>in</strong> (Ryu et al. 2002), where multiple MFs are located <strong>in</strong> each antennabranch. The outputs of MFs are added noncoherently, and the result is comparedto a threshold. The results <strong>in</strong>dicate that <strong>acquisition</strong> performance becomes improvedcont<strong>in</strong>ually as the number of antennas <strong>in</strong>creases, and the performance improvementdepends on the degree of spatial fad<strong>in</strong>g correlation. A generalized code <strong>acquisition</strong>scheme, where antennas and correlators are divided <strong>in</strong>to groups, and the signals arecomb<strong>in</strong>ed noncoherently afterwards is proposed <strong>in</strong> (Je et al. 2003, Sh<strong>in</strong> & Lee 2003).Such a code <strong>acquisition</strong> scheme, where multiple receive and transmit diversity antennasare exploited is studied <strong>in</strong> (Ikai et al. 1999). The authors found that the receiveantenna diversity is more effective than transmit antenna diversity. However, transmitdiversity is important s<strong>in</strong>ce it helps to reduce miss-<strong>acquisition</strong> probability. An <strong>in</strong>terest<strong>in</strong>gapproach of code <strong>acquisition</strong> with multiple diversity antennas is studied <strong>in</strong> (Yanget al. 1999). There<strong>in</strong>, a MF is located <strong>in</strong> each antenna branch and two different cases arestudied based on the <strong>in</strong>itial code phases of these MFs. In the first case, all MFs use thesame code phase and output signals of MFs are then comb<strong>in</strong>ed to improve SNR. In thesecond case, different code phases are used <strong>in</strong> each MF to <strong>in</strong>crease the parallelism ofthe search. The results <strong>in</strong>dicate that <strong>in</strong> low SNR scenarios, antenna comb<strong>in</strong><strong>in</strong>g is moreeffective while the parallel scheme is better <strong>in</strong> high SNR scenarios. Therefore, a com-43
<strong>in</strong>ation of both approaches is shown to provide the best performance for a particularSNR value. Based on these results, (Chang et al. 2000) proposed an adaptive schemethat adjusts the degree of comb<strong>in</strong><strong>in</strong>g without the explicit knowledge of the SNR. Adaptivecode <strong>acquisition</strong> schemes have also been studied <strong>in</strong> (Kwon et al. 2006, Kwon et al.2007).<strong>Code</strong> <strong>acquisition</strong> structures which utilize beamform<strong>in</strong>g have been studied <strong>in</strong> somepapers. One of the earliest papers deal<strong>in</strong>g with this subject has been published by Compton,who experimentally <strong>in</strong>vestigated the characteristics of an adaptive analog array <strong>in</strong>the presence of s<strong>in</strong>usoidal <strong>in</strong>terference while receiv<strong>in</strong>g a DS/SS signal (Compton 1978).He presents performance results <strong>in</strong> terms of the amount of <strong>in</strong>terference rejection andoutput SNR. The maximum likelihood delay estimator of a receiver us<strong>in</strong>g an antennaarray is derived <strong>in</strong> (Dlugos & Scholtz 1989). A serial search code <strong>acquisition</strong> <strong>in</strong> thedelay doma<strong>in</strong> is extended also to <strong>in</strong>clude the angular doma<strong>in</strong> <strong>in</strong> (Katz et al. 2001b, Katz2002). The result is a two dimensional (delay – angle) serial search strategy where onlya s<strong>in</strong>gle correlator is needed. The receiver <strong>in</strong> (Katz et al. 2001b) generates orthogonalfixed beams us<strong>in</strong>g a Butler matrix and evaluates these beams us<strong>in</strong>g a serial search techniquethat results <strong>in</strong> jo<strong>in</strong>t delay and DOA estimates. The study is extended to cover alsononuniform spatial distributions of <strong>in</strong>terference (Katz et al. 2000, Katz et al. 2001a,Katz et al. 2004). Authors show that the <strong>acquisition</strong> performance is clearly degraded<strong>in</strong> this new scenario, but this degradation can be effectively reduced us<strong>in</strong>g a differenttype of search strategy, adaptive <strong>in</strong>tegration time, and adaptive threshold sett<strong>in</strong>g. Acquisitionprocedures where a correlator is used <strong>in</strong> each antenna are studied <strong>in</strong> (Wang &Kwon 2000a, Wang & Kwon 2000b, Wang & Kwon 2003b and Wang & Kwon 2003a).The outputs of the correlators are comb<strong>in</strong>ed us<strong>in</strong>g beamform<strong>in</strong>g. The weights of thebeamformer are obta<strong>in</strong>ed via LMS adaptation us<strong>in</strong>g a pilot channel, i.e., the DOA <strong>in</strong>formationis not needed. The pr<strong>in</strong>ciple of utiliz<strong>in</strong>g MMSE beamform<strong>in</strong>g dur<strong>in</strong>g code<strong>acquisition</strong> is published <strong>in</strong> (Zhang et al. 2003 and Zhang et al. 2004). A code <strong>acquisition</strong>scheme employ<strong>in</strong>g an adaptive beamformer and an adaptive temporal filter is proposed<strong>in</strong> (Yang & Wu 2005, Yang & Wu 2007). A technique that comb<strong>in</strong>es temporal filter<strong>in</strong>gand spatial beamform<strong>in</strong>g for jo<strong>in</strong>t PN code <strong>acquisition</strong> and DOA estimation is proposed<strong>in</strong> (Chuang et al. 2003). The results show that the proposed solution is resistant to thenear-far effect and robust aga<strong>in</strong>st the change of the fad<strong>in</strong>g environment.A performance comparison of code <strong>acquisition</strong> us<strong>in</strong>g antenna diversity and beamform<strong>in</strong>gis studied <strong>in</strong> (Katz et al. 2002a). The authors found that <strong>in</strong> low SNR scenarios,the diversity scheme provides the best performance. As SNR <strong>in</strong>creases, both beamform-44
<strong>in</strong>g and diversity schemes will atta<strong>in</strong> similar performance. There are also papers (e.g.,van der Veen et al. 1998, Raleigh & Boros 1998, Gu et al. 2001, Khalaj et al. 1994,Kataoka et al. 2004, Rui & Ristaniemi 2005) which consider jo<strong>in</strong>t angle and delay estimation.Based on this literature review, it can be concluded that the research problemswhich are covered <strong>in</strong> this thesis have not been published previously.45
3 Smart antennasA smart antenna (i.e., adaptive antenna) is an electronically controllable antenna array,whose radiation pattern can be adjusted. This feature will <strong>in</strong>crease overall system capacitybecause different users can be separated <strong>in</strong> the spatial doma<strong>in</strong> (i.e., SDMA), althoughthey would use the same time, frequency and <strong>spread</strong><strong>in</strong>g code (Saunders 1999). In additionto capacity improvement, smart antennas improve also the peak data rate, averagethroughput, coverage and spectral efficiency of cellular <strong>systems</strong> (Liberti & Rappaport1999). Smart antennas can be used for <strong>in</strong>terference cancellation purposes by plac<strong>in</strong>g themaximum ga<strong>in</strong> of the radiation pattern toward the desired signal and m<strong>in</strong>imum ga<strong>in</strong>s(i.e., nulls) toward <strong>in</strong>terfer<strong>in</strong>g signals. By means of an appropriate adaptive algorithm,the radiation pattern can follow sources of <strong>in</strong>terfer<strong>in</strong>g signals although they are mov<strong>in</strong>g.In conclusion, significant improvements both <strong>in</strong> l<strong>in</strong>k and network level can be atta<strong>in</strong>edby employ<strong>in</strong>g smart antennas.Beamform<strong>in</strong>g can be analog or digital. Analog beamform<strong>in</strong>g is performed either<strong>in</strong> RF or <strong>in</strong> <strong>in</strong>termediate frequency (IF) us<strong>in</strong>g e.g., a Butler-matrix (Litva & Lo 1996,Hansen 1998, Liberti & Rappaport 1999). Digital beamform<strong>in</strong>g is performed either<strong>in</strong> the baseband or <strong>in</strong> IF, but not until analog-to-digital conversion (A/D) (Litva & Lo1996, Liberti & Rappaport 1999). The use of digital beamform<strong>in</strong>g has been generalizedlately due to the development of powerful signal process<strong>in</strong>g <strong>systems</strong>. Therefore, onlydigital beamform<strong>in</strong>g is covered <strong>in</strong> this thesis. Beamform<strong>in</strong>g can be carried out <strong>in</strong> boththe transmit and receive <strong>direct</strong>ion. Only the receiv<strong>in</strong>g aspect of beamform<strong>in</strong>g is covered<strong>in</strong> this work, s<strong>in</strong>ce the focus is on synchronization.3.1 Digital beamform<strong>in</strong>g algorithmsSmart antennas can be used for <strong>in</strong>terference cancellation by us<strong>in</strong>g different beamform<strong>in</strong>galgorithms. The pr<strong>in</strong>ciple of spatial beamform<strong>in</strong>g is illustrated <strong>in</strong> Fig. 5. The termspatial beamform<strong>in</strong>g means that there is only one weight coefficient after each antennaelement. Then the array output signal can be written asy(n)=M∑m=1w ∗ mx m (n), (8)47
where x 1 (n),···,x M (n) are discrete time signals obta<strong>in</strong>ed after downconversion from(1), w 1 ,···,w M are adjustable complex valued weight coefficients of the beamformer,and (·) ∗ denotes complex conjugate. The array output signal can be presented <strong>in</strong> vectorform asy(n)=w H x(n), (9)where w =[w 1 w 2 ··· w M ] T , x(n)=[x 1 (n) x 2 (n) ··· x M (n)] T , and (·) H denotes complexconjugate transpose.x 1 (n)w ⋆ 1x m (n)w ⋆ m∑output y(n)x M (n)w ⋆ MFig 5. Pr<strong>in</strong>ciple of spatial beamform<strong>in</strong>g (Godara 1997).3.1.1 Classical beamform<strong>in</strong>gIn the classical beamform<strong>in</strong>g, which is also known as the Bartlett’s or conventionalbeamform<strong>in</strong>g, the maximum ga<strong>in</strong> of a radiation pattern is <strong>direct</strong>ed toward the desiredsignal, but m<strong>in</strong>imum ga<strong>in</strong>s toward <strong>in</strong>terfer<strong>in</strong>g signals are not attempted. Therefore,the DOA of the desired signal must be known. Maximum ga<strong>in</strong> is achieved by us<strong>in</strong>gthe steer<strong>in</strong>g vector of the desired signal as the weight coefficient vector i.e., w = a(θ 0 ).Then a signal arriv<strong>in</strong>g from <strong>direct</strong>ion θ 0 becomes comb<strong>in</strong>ed coherently. This means thatthe antenna array ga<strong>in</strong>, which is def<strong>in</strong>ed as SNR improvement, is equal to the numberof antenna elements <strong>in</strong> the array. (Godara 1997)3.1.2 Nullsteer<strong>in</strong>gNullsteer<strong>in</strong>g is another simple beamform<strong>in</strong>g algorithm. Its purpose is to shape the radiationpattern so that maximum ga<strong>in</strong> is <strong>direct</strong>ed toward the desired signal and m<strong>in</strong>imum48
po<strong>in</strong>ts toward <strong>in</strong>terfer<strong>in</strong>g signals. Maximum ga<strong>in</strong> is obta<strong>in</strong>ed by select<strong>in</strong>gw H a(θ 0 )=1, (10)as <strong>in</strong> classical beamform<strong>in</strong>g. M<strong>in</strong>imum ga<strong>in</strong>s toward <strong>in</strong>terfer<strong>in</strong>g signals are obta<strong>in</strong>edfrom the follow<strong>in</strong>g constra<strong>in</strong>tsw H a(θ i )=0, i = 1,···,K, (11)where θ 1 ···θ K are DOAs of <strong>in</strong>terfer<strong>in</strong>g signals and K is their number. Thus, the nullsteer<strong>in</strong>gmethod has a disadvantage that it requires DOA <strong>in</strong>formation from all signals.The weight coefficient vector of the nullsteer<strong>in</strong>g beamformer can be obta<strong>in</strong>ed fromw H = e T A −1 , (12)where A =[a(θ 0 ) a(θ 1 ) ··· a(θ K )] conta<strong>in</strong>s steer<strong>in</strong>g vectors on its columns and e =[10··· 0] T conta<strong>in</strong>s the constra<strong>in</strong>ts from (10) and (11). Equation (12) requires A to be<strong>in</strong>vertible. If A is not a square matrix, we can use the estimatew H = e T A H (AA H ) −1 , (13)where A H (AA H ) −1 is a so called pseudo<strong>in</strong>verse. (Godara 1997)Although nullsteer<strong>in</strong>g beamform<strong>in</strong>g produces nulls toward <strong>in</strong>terfer<strong>in</strong>g signals, it isnot designed to m<strong>in</strong>imize uncorrelated noise at the output of the array. Thus, it is asuboptimal method because it does not maximize the signal-to-<strong>in</strong>terference-plus-noiseratio (SINR) at the output of the beamformer. (Godara 1997)3.1.3 M<strong>in</strong>imum Variance Distortionless ResponseIn m<strong>in</strong>imum variance distortionless response (MVDR) beamform<strong>in</strong>g, the basic pr<strong>in</strong>cipleis to m<strong>in</strong>imize the output power of the array. This m<strong>in</strong>imization is done conditionallyso that the ga<strong>in</strong> <strong>in</strong> the <strong>direct</strong>ion of the desired signal is kept constant, mean<strong>in</strong>gthat the desired signal is passed without distortion. The weight coefficient vector of theMVDR method can be solved us<strong>in</strong>g Lagrange multipliers and its derivation is presented<strong>in</strong> (Hayk<strong>in</strong> 1996). The weight coefficients can be solved fromw =R −1 a(θ 0 )a H (θ 0 )R −1 a(θ 0 ) , (14)49
where R = E{x(n)x H (n)} is the array correlation matrix, and R −1 is its <strong>in</strong>verse. E{·}denotes expectation. Elements of the array correlation matrix describe correlations betweensignals <strong>in</strong> different antennas. An array correlation matrix can be estimated fromdata us<strong>in</strong>g a simple averag<strong>in</strong>g asˆR = 1 NN∑i=1x(i)x H (i). (15)It can be noticed from (14) that only the DOA of the desired signal must be known.Interfer<strong>in</strong>g signals appear <strong>in</strong> R with large correlation values, and they are removed <strong>in</strong>MVDR beamform<strong>in</strong>g although their DOAs would be unknown.3.1.4 MaxSINRA commonly used optimization criteria is to maximize SINR at the output of the antennaarray. This method has such a disadvantage that the correlation matrix estimation hasto be done at the time when the desired signal is absent. Due to this restriction, themaxSINR algorithm can be used ma<strong>in</strong>ly <strong>in</strong> radar applications. If this k<strong>in</strong>d of correlationmatrix estimation is possible, the weight coefficient vector can be written as (Godara1997)R −1INw =a(θ 0)a H (θ 0 )R −1IN a(θ 0) , (16)where (R IN ) is the correlation matrix of noise and <strong>in</strong>terference. The correlation matrixR can be written as R = R S + R IN , where R S is the correlation matrix of the desiredsignal. If strong <strong>in</strong>terfer<strong>in</strong>g signals are disturb<strong>in</strong>g the DS/SS system, the term R INdom<strong>in</strong>ates s<strong>in</strong>ce R S consists of small values due to the low power <strong>spectrum</strong> densityof a DS/SS signal. This means that R ≈ R IN . Therefore, the MVDR and maxSINRalgorithms have almost the same weight coefficients <strong>in</strong> this case.3.1.5 Whiten<strong>in</strong>g filterA whiten<strong>in</strong>g filter tries to enforce a white <strong>spectrum</strong> <strong>in</strong> its output. It has been used asa time doma<strong>in</strong> <strong>in</strong>terference suppressor <strong>in</strong> (Proakis 1996). It can be applied for DS/SSsignals because their <strong>spectrum</strong> is like white noise. Therefore, a whiten<strong>in</strong>g filter cannotestimate a DS/SS signal, but narrowband <strong>in</strong>terfer<strong>in</strong>g signals can be estimated andremoved. The restriction on <strong>in</strong>terference cancellation <strong>in</strong> the time doma<strong>in</strong> is that the50
andwidth of the <strong>in</strong>terfer<strong>in</strong>g signal must be less than 50 % of the bandwidth of theDS/SS-signal (Saarnisaari 2001). The idea of a whiten<strong>in</strong>g filter<strong>in</strong>g can be applied <strong>in</strong>antenna arrays too, but then we are not restricted to elim<strong>in</strong>at<strong>in</strong>g narrowband <strong>in</strong>terfer<strong>in</strong>gsignals due to spatial process<strong>in</strong>g. Beamform<strong>in</strong>g based on a whiten<strong>in</strong>g filter doesnot need DOA <strong>in</strong>formation, but it has also two disadvantages compared to many otherbeamform<strong>in</strong>g algorithms. Firstly, an <strong>acquisition</strong> decision will be based on the output ofone antenna, i.e., antenna comb<strong>in</strong><strong>in</strong>g ga<strong>in</strong> is not atta<strong>in</strong>ed. This means that higher transmissionpower is needed, or sensitivity is lost if compared to the other beamform<strong>in</strong>gtechniques. Secondly, the desired DS/SS signal will also be removed if its SNR is toohigh.3.1.6 M<strong>in</strong>imum Mean Square ErrorIn adaptive filter theory, one of the most general optimization criteria is the m<strong>in</strong>imummean square error (MMSE), where the filter’s weight coefficients are def<strong>in</strong>ed by m<strong>in</strong>imiz<strong>in</strong>gthe mean squared error between tra<strong>in</strong><strong>in</strong>g and filter output signals (Hayk<strong>in</strong> 1996).The MMSE algorithm can be applied also <strong>in</strong> beamform<strong>in</strong>g, because it is just filter<strong>in</strong>g<strong>in</strong> the spatial doma<strong>in</strong>. However, <strong>in</strong> <strong>in</strong>itial code <strong>acquisition</strong>, such a tra<strong>in</strong><strong>in</strong>g <strong>sequence</strong>cannot be used which will be based on the detected data symbols, because the receiveris unable to detect symbols before code synchronization. Instead, the receiver can utilizethe known PN <strong>spread</strong><strong>in</strong>g code as a tra<strong>in</strong><strong>in</strong>g signal. The weight coefficient vector ofMMSE beamform<strong>in</strong>g can be obta<strong>in</strong>ed from the Wiener filter theory as (Hayk<strong>in</strong> 1996)w = R −1 d, (17)where d = E{x(n)c ∗ (n)} is a cross-correlation vector between the tra<strong>in</strong><strong>in</strong>g and array<strong>in</strong>put signals. The MMSE beamformer has the advantage that it does not need DOA<strong>in</strong>formation at all. Therefore, the MMSE beamform<strong>in</strong>g and whiten<strong>in</strong>g filter are unsensitiveto nonidealities like calibration errors. If these algorithms are used <strong>in</strong> code<strong>acquisition</strong>, then a two-dimensional (delay – angle) search is not required.3.1.7 Space-time beamform<strong>in</strong>gIn spatial or narrowband beamform<strong>in</strong>g, there is only one weight coefficient after eachantenna element, whereas <strong>in</strong> space-time or wideband beamform<strong>in</strong>g there are multipleweight coefficients as is illustrated <strong>in</strong> Fig. 6. A narrowband beamformer is able to51
form nulls exactly only at a s<strong>in</strong>gle frequency, because the steer<strong>in</strong>g vector is functionof frequency and antenna spac<strong>in</strong>g (Rodgers & R. T. Compton 1979, Compton 1988).In space-time process<strong>in</strong>g, each of M antennas is followed by a P-tap f<strong>in</strong>ite impulseresponse (FIR) filter, i.e., a tapped delay l<strong>in</strong>e (TDL). This structure allows each antennaelement to have a phase response which can be adjusted as a function of frequency. Thismakes it possible to compensate different phase shifts of different frequencies (Liberti& Rappaport 1999). Bandwidth performance of adaptive arrays with tapped delay-l<strong>in</strong>eprocess<strong>in</strong>g is studied <strong>in</strong> (Vook & R. T. Compton 1992, and references there<strong>in</strong>).Another advantage of space-time process<strong>in</strong>g is that it allows <strong>in</strong>terference cancellation<strong>in</strong> the frequency or time doma<strong>in</strong>, as well as <strong>in</strong> the spatial doma<strong>in</strong>, by optimiz<strong>in</strong>g allMP taps. In space-time adaptive process<strong>in</strong>g (STAP), P sample vectors (i.e., snapshotsx(n)) are <strong>in</strong>cluded <strong>in</strong>to a space-time sample vector asx ST (n)=[x T (n)···x T (n − P + 1)] T . (18)A space-time steer<strong>in</strong>g vector can be constructed as (Saarnisaari et al. 2005)a ST (θ 0 )=[0···a(θ 0 ) T ···0] T , (19)which conta<strong>in</strong>s a spatial steer<strong>in</strong>g vector at the center and zeros elsewhere. If a spacetimesteer<strong>in</strong>g vector is constructed <strong>in</strong> this way, it avoids <strong>in</strong>ter chip <strong>in</strong>terference (ICI).An estimate of space-time correlation matrix ( ˆR ST ) can be achieved from (15) whenx ST (n) <strong>in</strong>stead of x(n) is used there. The same beamform<strong>in</strong>g algorithms that werejust described <strong>in</strong> the context of spatial beamform<strong>in</strong>g can also be applied <strong>in</strong> space-timebeamform<strong>in</strong>g. This means that the spatial doma<strong>in</strong> steer<strong>in</strong>g vector, the sample vector,and the correlation matrix must be replaced with their space-time counterparts.52
x 1 (n)w ⋆ 11w ⋆ 1Px m (n)w ⋆ m1w ⋆ mP∑output y(n)x M (n)w ⋆ M1w ⋆ MPFig 6. Pr<strong>in</strong>ciple of space-time beamform<strong>in</strong>g.3.1.8 Beam-space techniquesIn beam-space process<strong>in</strong>g, the signals <strong>in</strong> different antennas are not weighted <strong>direct</strong>ly as<strong>in</strong> the aforementioned element-space process<strong>in</strong>g. Instead, the set of orthogonal beamsare generated and outputs of these beams are weighted. The number of beams utilized<strong>in</strong> the beam-space process<strong>in</strong>g is smaller or equal to the number of antenna elements. Ifthe number of beams is smaller than the number of antenna elements, the number ofrequired weight coefficients is also smaller than <strong>in</strong> element-space process<strong>in</strong>g. This isone advantage of the beam-space process<strong>in</strong>g, s<strong>in</strong>ce it reduces computational load. Anotheradvantage is its robustness aga<strong>in</strong>st non-idealities of implementation. Beam-spaceprocess<strong>in</strong>g has been studied <strong>in</strong> (Godara 1997, and references there<strong>in</strong>). The beam-spaceversions of different DOA estimation methods are studied <strong>in</strong> (Zoltowski et al. 1991,Zoltowski et al. 1993, Mathews & Zoltowski 1994, Trees 2002). The generalized sidelobecanceller (GSC) is an example of beam-space beamform<strong>in</strong>g and its exact descriptioncan be found from (Hayk<strong>in</strong> 1996). The basic idea is to construct a ma<strong>in</strong>beam byus<strong>in</strong>g the <strong>in</strong>formation that is on hand <strong>in</strong> advance. Then auxiliary beams orthogonal tothe ma<strong>in</strong>beam are produced. By weight<strong>in</strong>g these auxiliary beams appropriately, those<strong>in</strong>terfer<strong>in</strong>g signals which are located <strong>in</strong> the sidelobes can be cancelled.53
3.2 DOA estimation algorithmsIn this section, a short overview of different DOA estimation algorithms will be presented.These algorithms can be broadly divided <strong>in</strong>to three different categories: conventionaltechniques, subspace based techniques and maximum likelihood techniques.Next, the algorithms <strong>in</strong>side those categories are briefly described.3.2.1 Conventional techniquesThe conventional DOA estimation techniques are based on the digital beamform<strong>in</strong>galgorithms, and they require a large number of antenna elements to achieve high resolution.These techniques electronically steer beams <strong>in</strong> all possible <strong>direct</strong>ions and look forpeaks <strong>in</strong> the output power. (Liberti & Rappaport 1999)Bartlett’s methodOne of the simplest DOA estimation techniques is Bartlett’s method, where the spatialuncerta<strong>in</strong>ty region is scanned with power measurement purposes for all <strong>direct</strong>ions us<strong>in</strong>gBartlett’s (i.e., classical) beamform<strong>in</strong>g. After scann<strong>in</strong>g, the so-called Bartlett’s spatial<strong>spectrum</strong> can be expressed asP Bartlett (θ)=w H Rw = a H (θ)Ra(θ). (20)Because the spatial <strong>spectrum</strong> represents received power as a function of DOA, thoseDOAs which correspond to maximum po<strong>in</strong>ts are considered as the actual DOAs ofarriv<strong>in</strong>g signals. This technique has poor spatial resolution due to large beamwidth andhigh sidelobes, if there are multiple signals. Resolution can be <strong>in</strong>creased by add<strong>in</strong>gmore antenna elements. (Liberti & Rappaport 1999)Capon’s methodCapon’s m<strong>in</strong>imum variance technique (Capon 1969) is based on Capon’s or MVDRbeamform<strong>in</strong>g, which was described <strong>in</strong> Section 3.1.3. Capon’s DOA estimator has betterspatial resolution than Bartlett’s method has, and its spatial <strong>spectrum</strong> can be written toform1P Capon (θ)=a H (θ)R −1 a(θ) . (21)54
The DOAs can be estimated by locat<strong>in</strong>g the peaks of the <strong>spectrum</strong>, as was done <strong>in</strong>Bartlett’s method. (Liberti & Rappaport 1999)3.2.2 Subspace based techniquesMost of the resolution limitations of the conventional DOA estimation techniques arisebecause they do not exploit the eigen structure of the <strong>in</strong>put data matrix. Subspace basedtechniques exploit this structure, which makes it possible to f<strong>in</strong>d out high resolutionalgorithms.MUSIC algorithmSchmidt proposed the multiple signal classification (MUSIC) algorithm (Schmidt 1979),which provides <strong>in</strong>formation about the number of <strong>in</strong>cident signals, DOAs, strengths,cross correlations, noise power, etc. The MUSIC algorithm is based on the eigenvaluedecomposition of correlation matrix R. Eigenvectors of R are separated <strong>in</strong>to two orthogonalsubspaces, called the signal subspace and noise subspace. If those eigenvectorswhich belongs to the noise subspace are <strong>in</strong>cluded <strong>in</strong> matrix V n , then DOAs of arriv<strong>in</strong>gsignals can be estimated by locat<strong>in</strong>g peaks from a MUSIC spatial <strong>spectrum</strong> given by1P MUSIC (θ)=a H (θ)V n V H n a(θ) . (22)The results shown <strong>in</strong> (Liberti & Rappaport 1999) <strong>in</strong>dicate that the MUSIC algorithmcan resolve also closely spaced signals which cannot be detected by Capon’smethod. When arriv<strong>in</strong>g signals are highly correlated, then MUSIC algorithm fails. Spatialsmooth<strong>in</strong>g techniques have been proposed to handle also highly correlated signals(Shan et al. 1985, Takao & Kikuma 1987). Various modifications to the MUSIC algorithmhave been proposed to <strong>in</strong>crease its resolution performance and to decrease itscomputational complexity. One such modification is the Root-MUSIC algorithm (Barabell1983), which is based on polynomial root<strong>in</strong>g and it provides higher resolution. TheRoot-MUSIC algorithm has an advantage that it does not require scann<strong>in</strong>g through all<strong>direct</strong>ions, because unknown DOAs can be solved mathematically from polynomialroots. One disadvantage is its applicability only for a uniform l<strong>in</strong>ear array (ULA),where antenna elements have been spaced at regular <strong>in</strong>tervals along a l<strong>in</strong>e. Anothermodification is the Cyclic MUSIC algorithm (Schell et al. 1989), which exploits thespectral coherence properties of signals. Fast subspace decomposition techniques have55
een studied <strong>in</strong> (Xu & Kailath 1994) to decrease the computational complexity of theMUSIC algorithm.ESPRIT algorithmEstimation of signal parameters via rotational <strong>in</strong>variance techniques (ESPRIT) is anothersubspace-based DOA estimation technique <strong>in</strong>troduced <strong>in</strong> (Roy & Kailath 1989).The ESPRIT algorithm notably reduces the computational and storage requirements ofthe MUSIC algorithm and it does not require scann<strong>in</strong>g through all <strong>direct</strong>ions to estimateDOAs. The ESPRIT algorithm requires that an antenna array be decomposed <strong>in</strong>totwo equal-sized identical subarrays. The exact description of the ESPRIT algorithm ispresented <strong>in</strong> (Liberti & Rappaport 1999).3.2.3 Maximum likelihood techniquesThe maximum likelihood DOA estimation techniques (Schweppe 1968, Li & Compton1993) are optimal techniques <strong>in</strong> the sense that they produce the most accurate DOAestimates, but they are computationally very <strong>in</strong>tensive. Therefore, the ML techniqueshave been less popular than the suboptimal subspace techniques. However, the MLtechniques have a superior performance compared to the subspace techniques, especially<strong>in</strong> low SNR conditions or when the number of signal samples is small (Zisk<strong>in</strong>d &Wax 1988). In addition, the ML techniques can also perform well <strong>in</strong> correlated signalconditions, whereas the subspace based methods fail <strong>in</strong> this situation.3.2.4 Detection of number of signalsMany DOA estimation algorithms require <strong>in</strong>formation about the number of arriv<strong>in</strong>gsignals. Therefore, estimation of the number of sources is an important issue. Themethods which are based on eigen decomposition are the most commonly used. Inthese methods, an estimate of the number of signals is obta<strong>in</strong>ed from the number ofrepeated smallest eigenvalues (Liberti & Rappaport 1999). One of the earliest methodsis an eigen-threshold method (Chen et al. 1991), which is based on threshold sett<strong>in</strong>g andhypothesis test<strong>in</strong>g. This method is computationally attractive, but the need for thresholdsett<strong>in</strong>g is a major disadvantage. Two other detection schemes are based on the Akaike<strong>in</strong>formation theoretic criteria (AIC) and the m<strong>in</strong>imum descriptive length (MDL) criteria56
(Wax & Kailath 1985). The latter methods do not require threshold sett<strong>in</strong>g. It is shown<strong>in</strong> (Wax & Kailath 1985) that the MDL method provides a consistent estimate of thenumber of signals, whereas the AIC method tends to overestimate the number of signals.However, the eigen-threshold method has better performance than the AIC and MDLmethods have (Chen et al. 1991). An alternative scheme for estimat<strong>in</strong>g the number ofsignals uses eigenvectors <strong>in</strong>stead of eigenvalues (Lee & Li 1994).57
4 Summary of the orig<strong>in</strong>al articles4.1 GeneralThe contents of the n<strong>in</strong>e orig<strong>in</strong>al articles are divided <strong>in</strong>to two groups. Articles I-IVconsider code <strong>acquisition</strong> <strong>in</strong> <strong>in</strong>tentional <strong>in</strong>terference by explor<strong>in</strong>g how different beamform<strong>in</strong>galgorithms can cope with narrowband and wideband <strong>in</strong>terfer<strong>in</strong>g signals, whenthe DOA of the desired DS/SS signal is assumed to be known. All <strong>in</strong>terfer<strong>in</strong>g signalsare highly <strong>direct</strong>ional, because each of them is arriv<strong>in</strong>g from a certa<strong>in</strong> <strong>direct</strong>ion <strong>in</strong>the spatial doma<strong>in</strong>. Moreover, they are located at the center frequency of the DS/SSsignal. This part of the study is based on computer simulations and such a receiverstructure is <strong>in</strong>vestigated where a s<strong>in</strong>gle MF is located after the beamform<strong>in</strong>g (BF) unit(i.e., BF+MF structure). The second group of Articles (V-IX) focuses on a theoreticalanalysis of synchronization probabilities and mean <strong>acquisition</strong> times when the DOA ofthe desired signal is unknown. In this part of the study, a BF+MF structure, as wellas a structure where MF is located <strong>in</strong> each antenna branch before beamform<strong>in</strong>g (i.e.,MF+BF structure) are <strong>in</strong>vestigated.4.2 <strong>Code</strong> <strong>acquisition</strong> performance under <strong>in</strong>tentional<strong>in</strong>terferenceArticle I deals with the <strong>in</strong>terference cancellation performance of different spatial doma<strong>in</strong>beamform<strong>in</strong>g algorithms, which were discussed <strong>in</strong> Section 3.1. The used BF+MFreceiver structure is shown <strong>in</strong> Fig. 7. The <strong>in</strong>vestigated spatial beamform<strong>in</strong>g algorithmsare: classical, nullsteer<strong>in</strong>g, MVDR, maxSINR, GSC and whiten<strong>in</strong>g filter. A ULA with8 antenna elements and half wavelength antenna separation is used. The <strong>in</strong>terferencescenario <strong>in</strong> the paper is such that there exists both narrowband and wideband <strong>in</strong>terfer<strong>in</strong>gsignals and I/S is 30 dB for each <strong>in</strong>terfer<strong>in</strong>g signal. Narrowband <strong>in</strong>terference is a s<strong>in</strong>usoidalwave and wideband <strong>in</strong>terfer<strong>in</strong>g signals have as large of a bandwidth as the DS/SSsignal has. However, wideband <strong>in</strong>terfer<strong>in</strong>g signals are assumed to be narrowband fromthe array signal process<strong>in</strong>g po<strong>in</strong>t of view, i.e., their fractional bandwidth W/ f c ≪ 1.Otherwise space-time beamform<strong>in</strong>g would be needed as was described <strong>in</strong> Section 3.1.7.This narrowband assumption is valid throughout the thesis. A Gold code of a length of59
127 chips is used as the <strong>spread</strong><strong>in</strong>g code and the performance measure is the RMSE ofthe delay estimate. Article I compares the performances of different spatial beamform<strong>in</strong>galgorithms and concludes that all the methods, except for classical beamform<strong>in</strong>g,are able to cancel multiple strong <strong>in</strong>terfer<strong>in</strong>g signals if they are not arriv<strong>in</strong>g from thesame <strong>direct</strong>ion as the desired signal. If <strong>in</strong>terfer<strong>in</strong>g and desired signal are arriv<strong>in</strong>g fromthe same <strong>direct</strong>ion (i.e., a case of ma<strong>in</strong>lobe <strong>in</strong>terference), then spatial process<strong>in</strong>g is nota sufficient <strong>in</strong>terference cancellation method.Which one of the <strong>in</strong>vestigated beamform<strong>in</strong>g algorithms should be used <strong>in</strong> practicedepends on the available a priori <strong>in</strong>formation. When there are strong <strong>in</strong>terfer<strong>in</strong>g signals,then classical beamform<strong>in</strong>g is not suitable for <strong>in</strong>terference cancellation. If DOAs ofdesired and <strong>in</strong>terfer<strong>in</strong>g signals are all known, then the nullsteer<strong>in</strong>g method is a goodchoice due to its simplicity. However, if only the DOA of the desired signal is known,then a more advanced MVDR or maxSINR method must be used. MMSE beamform<strong>in</strong>gis not <strong>in</strong>vestigated <strong>in</strong> Article I, but Article IX shows that it is a good choice when allDOAs are unknown.spaceorspace -timeBFMFdelayestimationFig 7. The BF+MF receiver structure. Modified from [II], © 2005 IEEE.The purpose of Article II is to <strong>in</strong>vestigate the <strong>in</strong>terference suppression capabilities ofthe follow<strong>in</strong>g three structures. The first structure is the spatial MVDR beamform<strong>in</strong>g,which was used also <strong>in</strong> Article I. The second structure is a comb<strong>in</strong>ation of a spatialMVDR beamformer and a separate <strong>in</strong>terference cancellation (IC) unit depicted <strong>in</strong> Fig.8. In this study, the used IC method is the FCME algorithm (Saarnisaari & Henttu2003). It operates here<strong>in</strong> <strong>in</strong> the frequency doma<strong>in</strong>, because the aim is to elim<strong>in</strong>ate narrowband<strong>in</strong>terfer<strong>in</strong>g signal arriv<strong>in</strong>g from the ma<strong>in</strong>lobe. The FCME algorithm operatesiteratively and <strong>in</strong> each iteration it elim<strong>in</strong>ates those frequency components whose poweris somewhat larger than the average power. The third strategy is the space-time MVDRbeamformer (Fig. 7), which utilizes multiple weight coefficients after each antenna ele-60
ment as was described <strong>in</strong> Section 3.1.7. The weight coefficient vector of the space-timeMVDR algorithm can be written <strong>in</strong>to the form (Saarnisaari et al. 2005)w ST =R −1ST a ST (θ 0 )a H ST (θ 0)R −1ST a ST (θ 0 ) , (23)which is a very similar solution as that found <strong>in</strong> spatial doma<strong>in</strong> MVDR process<strong>in</strong>g (14).Here<strong>in</strong>, R ST is a space-time correlation matrix and a ST (θ 0 ) is a space-time steer<strong>in</strong>gvector.spatialBFICMFdelayestimationFig 8. The BF+IC+MF receiver structure. Repr<strong>in</strong>ted from [II], © 2005 IEEE.In simulations of Article II, antenna array structure, <strong>spread</strong><strong>in</strong>g code and performancemeasure are the same as <strong>in</strong> Article I. The number of time taps after each antenna elementis 5 <strong>in</strong> space-time beamform<strong>in</strong>g. Performances of algorithms are compared <strong>in</strong> fourdifferent <strong>in</strong>terference scenarios. Research is started from s<strong>in</strong>usoidal <strong>in</strong>terference, thenfollows the cases where either 10 %, 20 %, or 40 % of the bandwidth of DS/SS signalis <strong>in</strong>terfered. The results aga<strong>in</strong> show that all the <strong>in</strong>vestigated <strong>in</strong>terference cancellationmethods can suppress those <strong>in</strong>terfer<strong>in</strong>g signals which are not arriv<strong>in</strong>g from the same <strong>direct</strong>ionas the desired signal. It is shown that the ability to suppress an <strong>in</strong>terfer<strong>in</strong>g signal,<strong>in</strong> closeness of a DS/SS signal, depends on the prevail<strong>in</strong>g SNR and I/S level. If SNRis high or I/S is low, then angle separation can be smaller. Naturally, this depends onthe antenna array structure used also. If it is desired that code <strong>acquisition</strong> succeeds also<strong>in</strong> the scenario where <strong>in</strong>terference arrives from the same <strong>direct</strong>ion as the desired signal,then the best alternative is to use space-time MVDR beamform<strong>in</strong>g. It gives a verygood protection aga<strong>in</strong>st all k<strong>in</strong>ds of <strong>in</strong>terference types. The use of the FCME <strong>in</strong>terferencecanceller after the spatial MVDR algorithm gives additional <strong>in</strong>terference toleranceaga<strong>in</strong>st ma<strong>in</strong>lobe <strong>in</strong>terference, when compared to the spatial MVDR process<strong>in</strong>g alone,but it does not have as good of a performance as the space-time MVDR algorithm has.61
Article III deals with the cancellation of impulsive <strong>in</strong>terfer<strong>in</strong>g signals us<strong>in</strong>g smartantennas. The receiver structure is depicted <strong>in</strong> Fig. 9, where the same space-timeMVDR algorithm is used as <strong>in</strong> Article II. There is also an additional <strong>in</strong>terference cancellationunit (pre-process<strong>in</strong>g algorithm), which is now located before beamform<strong>in</strong>g.That is because the space-time MVDR beamform<strong>in</strong>g cannot elim<strong>in</strong>ate impulses if theyare arriv<strong>in</strong>g from the ma<strong>in</strong>lobe. Four different pre-process<strong>in</strong>g algorithms which manipulatesnapshots before beamform<strong>in</strong>g are <strong>in</strong>vestigated. Two of them are algorithms whereeach snapshot is normalized either by its norm (Visuri et al. 2001) or norm squared. Theother two methods are the CME and FCME algorithms. The orig<strong>in</strong>al work of the CMEalgorithm (Henttu & Aromaa 2002) concentrated on excis<strong>in</strong>g narrowband <strong>in</strong>terference<strong>in</strong> the frequency doma<strong>in</strong>. This study <strong>in</strong>vestigates how well the CME algorithm canelim<strong>in</strong>ate impulses <strong>in</strong> the time doma<strong>in</strong>. The CME algorithm is an iterative <strong>in</strong>terferencecancellation method like the FCME. Impulse suppression capability is demonstratedthrough simulations. In the fixed DOA case, impulses are arriv<strong>in</strong>g 30 ◦ distance fromthe desired signal, and <strong>in</strong> the random DOAs case, they are randomly generated between±60 ◦ from the desired signal. Antenna array, <strong>spread</strong><strong>in</strong>g code and performance measureare the same as <strong>in</strong> Articles I and II. The results show that space-time MVDR beamform<strong>in</strong>galone can suppress impulses when they are arriv<strong>in</strong>g from a fixed DOA which,however, is different than the DOA of a desired signal. In the random DOAs case,a pre-process<strong>in</strong>g algorithm is required. The results <strong>in</strong>dicate that all <strong>in</strong>vestigated preprocess<strong>in</strong>galgorithms are powerful methods aga<strong>in</strong>st impulsive <strong>in</strong>terferences. In ArticleIV, a study of impulsive <strong>in</strong>terference cancellation is extended to cover also synchronizationprobabilities and mean <strong>acquisition</strong> times.pre-process<strong>in</strong>galgorithmspace-timeMVDRbeamformerMFdelayFig 9. The IC+BF+MF receiver structure. Modified from [III], © 2004 IEEE.As a conclusion of the research <strong>in</strong> these <strong>in</strong>terference scenarios, it is clear that the proper<strong>in</strong>terference cancellation method depends on the type of <strong>in</strong>terference, if the <strong>in</strong>terfer<strong>in</strong>gsignal is arriv<strong>in</strong>g from the same <strong>direct</strong>ion as the desired signal. Otherwise, the type62
of <strong>in</strong>terference is not a critical issue. Therefore, the number of signals, DOAs andstrengths of signals among other th<strong>in</strong>gs are important factors, if <strong>in</strong>terference cancellationis based on the use of adaptive antennas. Therefore, the selected <strong>in</strong>terferencecancellation method depends on a priori <strong>in</strong>formation obta<strong>in</strong>ed from the signal environment.4.3 Analysis of synchronization probabilities and mean<strong>acquisition</strong> times with smart antennasSo far <strong>in</strong> this thesis, consideration of smart antennas <strong>in</strong> code <strong>acquisition</strong> has ma<strong>in</strong>ly beenbased on computer simulations, and the performance measure used has been the RMSEof the time delay estimate. It can be concluded from those results that code <strong>acquisition</strong>succeeds us<strong>in</strong>g the beamform<strong>in</strong>g and <strong>in</strong>terference cancellation structures studied <strong>in</strong> ArticlesI-III. However, <strong>in</strong> those articles we are not <strong>in</strong>terested about the question of howmuch time it takes to perform <strong>acquisition</strong>. This section, and Articles IV-IX, focus onthe <strong>acquisition</strong> time. Therefore, the used performance measure is changed to the T MA ,which was described <strong>in</strong> Section 2.3. As it was discussed, T MA is amongst other th<strong>in</strong>gsa function of the synchronization probabilities P D and P FA . The uniform circular array(UCA) is used <strong>in</strong>stead of ULA <strong>in</strong> Articles V-IX, because DOA of the desired signalis also unknown there. An angular uncerta<strong>in</strong>ty of 360 ◦ is more easily covered us<strong>in</strong>g as<strong>in</strong>gle UCA, whereas three ULAs placed <strong>in</strong> a triangular form are typically needed todo the same task. It is possible to use other array structures too. However, the arraystructure and the number of antennas <strong>in</strong> the array have an <strong>in</strong>fluence, for <strong>in</strong>stance, onDOA estimation accuracy and <strong>in</strong>terference cancellation capability.It was assumed <strong>in</strong> Articles I-IV, that the DOA of the desired signal is exactly known<strong>in</strong> advance. This is not necessarily the situation <strong>in</strong> practise and thus, the effects of steer<strong>in</strong>gvector errors (i.e., DOA errors) <strong>in</strong>to code synchronization probabilities are <strong>in</strong>vestigated<strong>in</strong> Article V. DOA error is the error between actual and estimated DOA. It has animpact on the amount of achieved antenna array ga<strong>in</strong>, because the ma<strong>in</strong>lobe of radiationpattern is not steered exactly toward the actual DOA of the desired signal. In Article V,beamform<strong>in</strong>g is performed us<strong>in</strong>g either the classical or MVDR algorithm and a blockdiagram of the used receiver structure is depicted <strong>in</strong> Fig. 10. Probability density functionsat the output of squar<strong>in</strong>g operation Z 1 (n) are analyzed and then, probabilities P Dand P FA are derived. A loss of antenna array ga<strong>in</strong> affects the probability density func-63
tions and thus, the probabilities P D and P FA also. Moreover, advanced beamform<strong>in</strong>galgorithms like the MVDR start to suppress the desired signal also, if DOA error is toolarge. Analyzed synchronization probabilities are verified by simulations.The follow<strong>in</strong>g conclusions can be made from the results. As expected, the MVDRis more sensitive to DOA errors than the classical beamformer, especially at large DOAerrors and high SNR values. If DOA error is relatively small and SNR per chip (E c /N 0 )< – 5 dB, then there is no considerable difference between these two beamformers. IfDOA error is large, then the robustness of the MVDR aga<strong>in</strong>st DOA errors may be improvedby us<strong>in</strong>g, for example, the adaptive diagonal load<strong>in</strong>g (ADL) algorithm, whichwas used <strong>in</strong> this study. In the ADL algorithm, a ma<strong>in</strong> diagonal of correlation matrix Ris weighted by an adaptive load, which reduces the effects of DOA errors (Lilja & Saarnisaari2005). However, this load<strong>in</strong>g happens at the expense of decreased <strong>in</strong>terferencesuppression capability.x 1(t)x 1(n)x M(t)x M(n)DOA +BFy(n)MFz(n)|·| 2Z 1(n)MaxZ V (n)syncPDIMaxT H1 T HVFig 10. A block diagram of the BF+MF receiver structure used <strong>in</strong> Article V.Repr<strong>in</strong>ted from [VII], © 2009 IEEE.In Article VI, a receiver structure is <strong>in</strong>vestigated which consists of a smart antennafollowed by a s<strong>in</strong>gle correlator or a MF. A block diagram of the <strong>in</strong>vestigated structureis depicted <strong>in</strong> Fig. 11. When this k<strong>in</strong>d of receiver structure is used and the DOA ofthe desired signal is unknown, there is a two-dimensional <strong>acquisition</strong> problem becauseboth the angle and delay must be found. In this case, the whole angular uncerta<strong>in</strong>tyregion (360 ◦ ) can be divided <strong>in</strong>to small angular cells us<strong>in</strong>g the fixed beam techniques,like the Butler matrix. The strategy where the receiver searches through all angular anddelay cells via a serial search procedure was orig<strong>in</strong>ally proposed <strong>in</strong> (Katz et al. 2001b).Division of the uncerta<strong>in</strong>ty region <strong>in</strong>to angular and delay cells is illustrated <strong>in</strong> Fig. 12.In the Butler matrix case, the number of angular cells is equal to the number of antennas<strong>in</strong> the array.64
x 1(n)x M(n)DOA +BFy(n)(ii)(i)MFz(n)Z 1(n)Z V (n)|·| 2PDI0 (·)∑ τ dsyncc(n − τ l )T H1T HVFig 11. A block diagram of the receiver structure used <strong>in</strong> Article VI. Repr<strong>in</strong>ted from[VI], © 2008 IEEE.TxLL − 1angularcell 2angularcell 12angularcell M1Tx: Transmitt<strong>in</strong>g stationRx: Receiv<strong>in</strong>g stationRxL delay cellsM angular cellsFig 12. Pr<strong>in</strong>ciple of two-dimensional code <strong>acquisition</strong> with fixed beams (Katz et al.2001b).In Article VI, research work is expanded from fixed beams to cover also the advancedbeamform<strong>in</strong>g techniques which require more computation, but they offer significantlybetter <strong>in</strong>terference suppression capability. Also, such a method is proposed where theDOAs of arriv<strong>in</strong>g signals are estimated prior to the <strong>acquisition</strong> process. This may reduceangular uncerta<strong>in</strong>ty because the number of needed angular cells is equal to thenumber of arriv<strong>in</strong>g signals as is illustrated <strong>in</strong> Fig. 13. All these <strong>acquisition</strong> strategiesare compared <strong>in</strong> serial search <strong>acquisition</strong> us<strong>in</strong>g either a correlator or a MF <strong>acquisition</strong>. Amodel for code <strong>acquisition</strong> us<strong>in</strong>g DOA estimation and beamform<strong>in</strong>g <strong>in</strong> the MF <strong>acquisition</strong>case is presented, and an expression for the mean <strong>acquisition</strong> time is derived, whenalso the actions related to array signal process<strong>in</strong>g are taken <strong>in</strong>to account. In this partic-65
ular T MA analysis, it is assumed that there is no overlapp<strong>in</strong>g between adjacent beams.If overlapp<strong>in</strong>g is taken <strong>in</strong>to account, then a <strong>direct</strong>ional signal arriv<strong>in</strong>g precisely <strong>in</strong> the<strong>direct</strong>ion that beams overlap, will appear <strong>in</strong> both of the adjacent angular cells. This willhave a positive effect on the overall <strong>acquisition</strong> time performance. The obta<strong>in</strong>ed analyticalresults <strong>in</strong>dicate that <strong>in</strong> the AWGN channel, a s<strong>in</strong>gle antenna receiver has the bestT MA performance if SNR per bit (E b /N 0 ) is high. This is because the slightly time consum<strong>in</strong>garray signal process<strong>in</strong>g operations are not needed <strong>in</strong> the s<strong>in</strong>gle antenna receiver.However, s<strong>in</strong>gle antenna methods without any <strong>in</strong>terference cancellation are sensitive to<strong>in</strong>terference, whereas smart antennas are not. Moreover, a s<strong>in</strong>gle antenna receiver iseasily obta<strong>in</strong>ed from a multi-antenna receiver by ignor<strong>in</strong>g all but one of the antenna outputs.If E b /N 0 is low, then multi-antenna receivers typically outperform s<strong>in</strong>gle antennareceivers. SNR per bit <strong>in</strong> the dB-scale is obta<strong>in</strong>ed by add<strong>in</strong>g the process<strong>in</strong>g ga<strong>in</strong> of DScomb<strong>in</strong><strong>in</strong>g (PG = 10log 10 (L)) up to the SNR per chip (i.e., E b /N 0 = E c /N 0 + PG). Results<strong>in</strong>dicate also that the DOA estimation-based methods outperform the fixed beammethods, if the number of arriv<strong>in</strong>g signals is low. However, DOA estimation from thechip level may be difficult and time consum<strong>in</strong>g if E c /N 0 is very low, as is illustrated <strong>in</strong>Fig. 14. DOA estimation accuracy depends among other th<strong>in</strong>gs on the used DOA estimationalgorithm, array structure, number of antennas, E c /N 0 and the number of samplevectors used for correlation matrix estimation. Estimation of the DOA of DS/SS signalbecomes more difficult when the <strong>spread</strong><strong>in</strong>g code length L <strong>in</strong>creases, because SNR <strong>in</strong>the channel (E c /N 0 ) typically decreases. If L is large, more averag<strong>in</strong>g (i.e., more samplevectors) or higher transmission power must be used <strong>in</strong> the synchronization phase.If DOA estimation is not possible at all, either a whiten<strong>in</strong>g filter, MMSE or scann<strong>in</strong>gthrough the whole angular uncerta<strong>in</strong>ty region must be used. One solution to this DOAestimation problem is to utilize the structure where MFs are located <strong>in</strong> each antennaelement. This structure will be described <strong>in</strong> more detail when Article IX is summarized.It makes it possible to perform DOA estimation after de<strong>spread</strong><strong>in</strong>g when SNR is higher.66
TxLL − 1TxTx2Rx: Receiv<strong>in</strong>g stationRxTx: Transmitt<strong>in</strong>g station (found by DOA estimator)1L delay cellsN angular cellsFig 13. Pr<strong>in</strong>ciple of two-dimensional code <strong>acquisition</strong> with DOA estimation (Puskaet al. 2005).RMSE of DOA estimate <strong>in</strong> degrees403020100500 200040006000Number of sample vectors 8000−510000−10−25−20−15E /N [dB]c 0Fig 14. DOA estimation results from Article VI. Repr<strong>in</strong>ted from [VI], © 2008 IEEE.If the fixed beam techniques are used, there is an optimum number of receiv<strong>in</strong>g antennasat each SNR level for which the m<strong>in</strong>imum T MA is obta<strong>in</strong>ed, as is shown <strong>in</strong> Fig. 15. Inthat case, it would be <strong>in</strong>terest<strong>in</strong>g to develop an adaptive system that is based on theestimated SNR, and would try to use the appropriate number of receiv<strong>in</strong>g antennasaim<strong>in</strong>g at m<strong>in</strong>imiz<strong>in</strong>g T MA .67
T MA[divided by T b]2500200015001000L = 63P FA1= 10 −3B DOA= 1B NDOA= Mτ d= LT cT FAτ d= 100 τ dT ABFτ d= (500/L) τ dE b/N 0= 0 dB, Fixed beam (conv.)E b/N 0= 0 dB, Fixed beam (adv.)E b/N 0= 4 dB, Fixed beam (conv.)E b/N 0= 4 dB, Fixed beam (adv.)E b/N 0= 4 dB, DOA + adv.E b/N 0= 8 dB, Fixed beam (conv.)E b/N 0= 8 dB, Fixed beam (adv.)E b/N 0= 8 dB, DOA + adv.T DOA+ABFτ d= (1000/L) τ d50000 5 10 15 20 25Number of antennasFig 15. T MA results from Article VI. Repr<strong>in</strong>ted from [VI], © 2008 IEEE.Article VII expands multi-antenna code <strong>acquisition</strong> research from Article VI <strong>in</strong>to differentserial search and maximum selection strategies when the first threshold cross<strong>in</strong>gsare verified us<strong>in</strong>g post detection <strong>in</strong>tegration (PDI) and another threshold comparison.Their T MA performances are studied <strong>in</strong> the static, fast and slow fad<strong>in</strong>g channels. Thepresented T MA analysis is also derived under an assumption that there is no overlapp<strong>in</strong>gbetween adjacent beams. Article VII <strong>in</strong>troduces some novel ideas of perform<strong>in</strong>gmaximum selection together with verification. When PDI is used, a maximum samplecan be selected either before or/and after PDI. Also, the threshold comparison can belocated either before or/and after PDI, as is shown <strong>in</strong> Fig. 10. Therefore, there are altogether2 4 = 16 comb<strong>in</strong>ations of select<strong>in</strong>g a maximum sample and perform<strong>in</strong>g thresholdcomparison. However, most of those comb<strong>in</strong>ations are not reasonable, e.g., there is nosense to select a maximum value both before and after PDI, if threshold comparison isnot utilized at all. The 6 reasonable comb<strong>in</strong>ations are discussed:– Max(1), maximum selection before PDI,– Max(1) T H (1), maximum selection and threshold comparison before PDI,– Max(V ), maximum selection after PDI,68
– Max(V ) T H (V ), maximum selection and threshold comparison after PDI,– Max(1&V ) T H (1), maximum selection before and after PDI, and threshold comparisonbefore PDI,– Max(1&V ) T H (1&V ), maximum selection as well as threshold comparison beforeand after PDI.Also <strong>in</strong> the serial search techniques, the threshold comparison can be located eitherbefore or/and after PDI. Thus, the follow<strong>in</strong>g three comb<strong>in</strong>ations can be dist<strong>in</strong>guished:– SS T H (1), threshold comparison before PDI,– SS T H (V ), threshold comparison after PDI,– SS T H (1&V ), threshold comparison before and after PDI.Theoretical T MA results <strong>in</strong>dicate that the most appropriate method <strong>in</strong> each particularsituation depends on SNR, fad<strong>in</strong>g rate, and on the number of antennas. Different valuesof parameter set may have an effect on the order between methods. The authors purposeis to give a theoretical tool for analyz<strong>in</strong>g and compar<strong>in</strong>g different alternatives. Thesystem designers can use their own parameter set <strong>in</strong> the mean <strong>acquisition</strong> time equationsto select a suitable method for their purposes. In the fad<strong>in</strong>g channel case of Article VII,angular <strong>spread</strong>s of arriv<strong>in</strong>g signals are assumed to be small. Otherwise, there would bemultiple angular cells which lead to the correct code <strong>acquisition</strong> state. Therefore, thechannel with large angular <strong>spread</strong> has to be analyzed <strong>in</strong> a slightly different way.Because the amount of sample vectors needed for successful DOA estimation dependson the prevail<strong>in</strong>g SNR (Fig. 14), Article VIII <strong>in</strong>troduces an iterative method forcode <strong>acquisition</strong>. The presented method first performs DOA estimation, then beamform<strong>in</strong>gand f<strong>in</strong>ally makes a synchronization decision based on the maximum outputof the MF. The method starts by collect<strong>in</strong>g a small number of sample vectors for DOAestimation. If the DOA estimate is <strong>in</strong>accurate, the ma<strong>in</strong>beam of the radiation pattern issteered toward an erroneous <strong>direct</strong>ion and code <strong>acquisition</strong> has failed. After a fail, moredata is collected that improves DOA estimation accuracy. This process is repeated untilangle and delay <strong>acquisition</strong> succeeds. The results show that the proposed method offersvery short mean <strong>acquisition</strong> times with moderate SNR values. However, <strong>in</strong> this <strong>in</strong>itialstudy, only a DS/SS signal and AWGN are arriv<strong>in</strong>g to the receiver. The time consumedfor DOA estimation is assumed to be fixed <strong>in</strong> Articles VI - VII, but it is adjustable <strong>in</strong>Article VIII.A pr<strong>in</strong>ciple of utiliz<strong>in</strong>g MMSE beamform<strong>in</strong>g dur<strong>in</strong>g code <strong>acquisition</strong> is proposed<strong>in</strong> (Zhang et al. 2003, Zhang et al. 2004), where correlators are used <strong>in</strong>stead of MFs69
and the beamform<strong>in</strong>g weights are obta<strong>in</strong>ed via a pilot channel. Article IX <strong>in</strong>vestigates aMMSE beamform<strong>in</strong>g structure where only one PN code period is used as a tra<strong>in</strong><strong>in</strong>g signal.Then there must be either a MF or a correlator <strong>in</strong> each antenna branch separately.A block diagram of the used MF+BF receiver structure with MMSE beamform<strong>in</strong>g isshown <strong>in</strong> Fig. 16. Weight coefficient calculation <strong>in</strong> MMSE beamform<strong>in</strong>g requires thata cross-correlation vector d = E{x(n)c ∗ (n)} between <strong>spread</strong><strong>in</strong>g code and array <strong>in</strong>putsignals is estimated, as was described <strong>in</strong> (17). Us<strong>in</strong>g this receiver structure, an estimateof a cross-correlation vector is obta<strong>in</strong>ed by tak<strong>in</strong>g a sample vector from the outputs ofMFs. <strong>Code</strong> <strong>acquisition</strong> performance under <strong>in</strong>tentional <strong>in</strong>terference is studied <strong>in</strong> ArticleIX, when there are one or three wideband <strong>in</strong>terfer<strong>in</strong>g signals and <strong>acquisition</strong> is aided eitherby MMSE or MVDR beamform<strong>in</strong>g. The performance of a s<strong>in</strong>gle antenna receiverwithout any <strong>in</strong>terference suppression unit is considered as a reference. P D and T MA areused as performance measures. The performance comparisons between beamformersshow that the MVDR has better P D performance but worse T MA performance. Thiscan be expla<strong>in</strong>ed by DOA <strong>in</strong>formation, which is needed <strong>in</strong> MVDR but not <strong>in</strong> MMSEbeamform<strong>in</strong>g. Acquisition via the MVDR algorithm us<strong>in</strong>g a BF+MF structure needssearch<strong>in</strong>g through all DOAs (Fig. 12), which consumes so much time that T MA performanceof the MMSE beamformer becomes better. However, this advantage comes atthe expense of <strong>in</strong>creased receiver complexity due to multiple MFs. It is worth not<strong>in</strong>gthat the T MA results presented for the MVDR beamformer are overall times needed forboth angle and delay <strong>acquisition</strong>, whereas T MA results for the MMSE beamformer conta<strong>in</strong>only the time needed for delay <strong>acquisition</strong>. However, communication is possible<strong>in</strong> both cases, s<strong>in</strong>ce the receiver is time synchronized. On the other hand, DOA estimationis easier from the outputs of MFs after code synchronization, because SNR is thenhigher. This can be seen from Fig. 17, where the RMSE of a DOA estimate is presentedas a function of E c /N 0 .70
x 1 (n)MFy 1 (n)MMSE BFz(n)syncx M (n)MFy M (n)Fig 16. A block diagram of the MF+BF structure used <strong>in</strong> Article IX. Modified from[IX], © 2007 IEEE.T HRMSE of DOA estimate [<strong>in</strong> degrees]45403530252015105ULA 8L = 255I/S = 40 dBDS/SS signal, before MFDS/SS signal, after MFInterfer<strong>in</strong>g signal, before MFInterfer<strong>in</strong>g signal, after MFDOA (DS/SS signal) = 80 °DOA (<strong>in</strong>terference) = 110 °0−30 −25 −20 −15 −10 −5 0 5 10E c/N 0[dB]Fig 17. DOA estimation performance as a function of E c /N 0 us<strong>in</strong>g the Root-MUSICalgorithm.The MF+BF structure used <strong>in</strong> Article IX has the advantage that it does not need pilotsymbols if only one PN code period is used as a reference signal. However, if the communicationsystem utilizes a separate synchronization channel where, e.g., constant bitsare always transmitted, then MMSE beamform<strong>in</strong>g can take advantage of a longer tra<strong>in</strong><strong>in</strong>g<strong>sequence</strong> (Zhang et al. 2003, Zhang et al. 2004). In MF <strong>acquisition</strong>, this means thatmultiple PN code periods are comb<strong>in</strong>ed accord<strong>in</strong>g to pilot symbols before beamform-71
<strong>in</strong>g, as is illustrated <strong>in</strong> Fig. 18. P D performance improvements of this k<strong>in</strong>d of structure,without and with <strong>in</strong>terference, are shown <strong>in</strong> Figs. 19 and 20, when 1-4 code periods arecomb<strong>in</strong>ed. When two PN code periods are comb<strong>in</strong>ed, then P D performance is almostthe same as <strong>in</strong> MVDR beamform<strong>in</strong>g. If the tra<strong>in</strong><strong>in</strong>g signal length is more than two codeperiods, then the P D performance of the MMSE beamformer becomes better than that ofthe MVDR beamform<strong>in</strong>g. T MA performance also improves because P D becomes higherand only a small amount of additional process<strong>in</strong>g is required. This additional process<strong>in</strong>gtime does not affect the T MA equation of MMSE beamform<strong>in</strong>g, provid<strong>in</strong>g that thetime spent for code periods comb<strong>in</strong><strong>in</strong>g is smaller or equal than the time spent for <strong>in</strong>versecorrelation matrix estimation. The correlation matrix is typically estimated fromseveral hundreds of snapshots, so dur<strong>in</strong>g that time, few code periods can be comb<strong>in</strong>edwithout an effect on T MA .x 1 (n)MFy 1 (n)∑(·)MMSE BFz(n)syncx M (n)MFy M (n)∑(·)Fig 18. A block diagram of the MF+BF receiver structure with MMSE beamform<strong>in</strong>gand a pilot channel.T H72
1P FA= 10 −30.90.80.70.60.3M = 10.2MVDRMMSE 1 periodMMSE 2 periods0.1MMSE 3 periodsMMSE 4 periods0−30 −25 −20 −15 −10 −5 0E c/N 0[dB]P D0.50.4Fig 19. P D results of the pilot channel aided MMSE beamform<strong>in</strong>g without <strong>in</strong>terference.10.90.8P FA= 10 −3I/S = 40 dB0.70.6P D0.50.40.3M = 10.2MVDRMMSE 1 periodMMSE 2 periods0.1MMSE 3 periodsMMSE 4 periods0−30 −25 −20 −15 −10 −5 0E c/N 0Fig 20. P D results of the pilot channel aided MMSE beamform<strong>in</strong>g with <strong>in</strong>terference.73
5 Conclusion and discussionIn this thesis, code <strong>acquisition</strong> of the DS/SS system was studied when a smart antennawas used <strong>in</strong> the receiver. The thesis was divided <strong>in</strong>to three parts. In the first part, aliterature review of code <strong>acquisition</strong> was presented. There<strong>in</strong>, the basic code <strong>acquisition</strong>methods as well as different performance measures were studied. A review of the results<strong>in</strong> the literature under fad<strong>in</strong>g, data modulation, Doppler, <strong>in</strong>tentional <strong>in</strong>terference,multiple-access <strong>in</strong>terference, other system <strong>in</strong>terference and multiple antennas was given.In the second part of the thesis, an overview on smart antennas was presented, particularlyfocus<strong>in</strong>g on digital beamform<strong>in</strong>g and DOA estimation algorithms. The third partof the thesis concentrated on the author’s contribution to the field.Orig<strong>in</strong>al articles were classified accord<strong>in</strong>g to their contents <strong>in</strong>to two groups. Thefirst group (I-IV) covered DS/SS code <strong>acquisition</strong> performance under <strong>in</strong>tentional <strong>in</strong>terference,when the DOA of the desired signal was assumed to be known. <strong>Code</strong> <strong>acquisition</strong>performance was studied <strong>in</strong> many k<strong>in</strong>ds of <strong>in</strong>terference scenarios, because each ofthem might appear <strong>in</strong> practice. This part of the study was based on computer simulationsand the BF+MF was used as a receiver structure. The used performance measurewas ma<strong>in</strong>ly an RMSE of time delay estimate. The follow<strong>in</strong>g spatial beamform<strong>in</strong>g algorithmswere studied: classical, nullsteer<strong>in</strong>g, MVDR, maxSINR, GSC and whiten<strong>in</strong>gfilter. It was shown that most of them are capable of cancell<strong>in</strong>g multiple and differenttypes of <strong>in</strong>terfer<strong>in</strong>g signals, if angle separation between desired and <strong>in</strong>terfer<strong>in</strong>g signalsis sufficient. Required angle separation depends on the prevail<strong>in</strong>g SNR, I/S and on theused antenna array configuration. If SNR or the number of antennas is high, or I/Sis low, then angle separation can be smaller. Classical beamform<strong>in</strong>g was the only onethat is not capable of elim<strong>in</strong>at<strong>in</strong>g <strong>in</strong>terfer<strong>in</strong>g signals. If an <strong>in</strong>terfer<strong>in</strong>g signal is arriv<strong>in</strong>gfrom the same <strong>direct</strong>ion as the desired signal, then more complex methods, likespace-time process<strong>in</strong>g, must be applied. Also some other methods aga<strong>in</strong>st ma<strong>in</strong>lobe <strong>in</strong>terferencewere studied, like the method where a separate <strong>in</strong>terference cancellation unitwas located after the spatial beamformer. It turned out that this structure gives additionaltolerance aga<strong>in</strong>st ma<strong>in</strong>lobe <strong>in</strong>terference compared to spatial beamform<strong>in</strong>g, but itdoes not have such good performance as the space-time algorithm has. Cancellation ofimpulsive <strong>in</strong>terfer<strong>in</strong>g signals with smart antennas was studied us<strong>in</strong>g a structure, wherean additional <strong>in</strong>terference cancellation (i.e., pre-process<strong>in</strong>g) unit was located before the75
space-time beamform<strong>in</strong>g. The results <strong>in</strong>dicate that many pre-process<strong>in</strong>g algorithms arepowerful methods aga<strong>in</strong>st impulsive <strong>in</strong>terferences. It can be concluded from the resultscarried out <strong>in</strong> Articles I-IV, that <strong>in</strong> the case of ma<strong>in</strong>lobe <strong>in</strong>terference, the most suitable<strong>in</strong>terference cancellation method depends on the type of <strong>in</strong>terference and on the amountof a priori <strong>in</strong>formation about the signal environment.The second group of Articles (V-IX) focused on a theoretical analysis of synchronizationprobabilities and mean <strong>acquisition</strong> times. In this part of the study, the assumptionswere changed so that also the DOA of the desired signal was unknown. Becausethe mean <strong>acquisition</strong> time is a function of the synchronization probabilities P D and P FA ,the effects of DOA errors on these probabilities were researched. It was concludedfrom the results that the advanced beamform<strong>in</strong>g algorithms are more sensitive to DOAerrors than the classical beamformer. However, the robustness of the advanced beamformeraga<strong>in</strong>st DOA errors can be improved us<strong>in</strong>g e.g., the adaptive diagonal load<strong>in</strong>galgorithm. When BF+MF is used as a receiver structure, there is a two-dimensional <strong>acquisition</strong>problem because both angle and delay must be found. In this case, the wholeangular uncerta<strong>in</strong>ty region (360 ◦ ) can be divided <strong>in</strong>to multiple small angular cells us<strong>in</strong>gbeamform<strong>in</strong>g techniques as is proposed <strong>in</strong> the literature. In this thesis, the researchwork of that area was extended to cover also the advanced beamform<strong>in</strong>g techniques, becausethey offer <strong>in</strong>terference suppression capability. It was shown that code <strong>acquisition</strong>performance can be improved by add<strong>in</strong>g a DOA estimator <strong>in</strong>to the system, s<strong>in</strong>ce it reducesthe number of required beams if the number of arriv<strong>in</strong>g signals is low. However,estimation of the DOA of DS/SS signal may be difficult if SNR <strong>in</strong> the channel is verylow, i.e., if the length of the <strong>spread</strong><strong>in</strong>g code is large. In that case, the methods whichdo not need DOA <strong>in</strong>formation must naturally be used. Also, an iterative method forcode <strong>acquisition</strong> was proposed, where the amount of data used for DOA estimation isprogressively <strong>in</strong>creased. The method was shown to offer short mean <strong>acquisition</strong> timeswith moderate SNR values. In addition, a MMSE beamform<strong>in</strong>g structure, where onlyone period of the known <strong>spread</strong><strong>in</strong>g code is used as a tra<strong>in</strong><strong>in</strong>g signal, was proposed. Thisstructure requires that there must be a correlator or a MF <strong>in</strong> each antenna branch separately.The method was shown to have worse P D but better T MA performance than adelay-angle method has. Worse P D performance comes from noncoherent antenna comb<strong>in</strong><strong>in</strong>gwhereas the better T MA performance is a con<strong>sequence</strong> from the fact that DOA<strong>in</strong>formation is not needed <strong>in</strong> MMSE beamform<strong>in</strong>g. Thus, it avoids time consum<strong>in</strong>gsearch<strong>in</strong>g through all angular cells. It was also shown that the P D and T MA performanceof the MMSE beamform<strong>in</strong>g can be improved, if the communication system <strong>in</strong>volves a76
separate channel for synchronization. Then, a longer tra<strong>in</strong><strong>in</strong>g <strong>sequence</strong> can be exploitedalready <strong>in</strong> the <strong>in</strong>itial code <strong>acquisition</strong> stage.As a conclusion of the research performed <strong>in</strong> this thesis, it is important to highlightthe fact that there is not a s<strong>in</strong>gle approach which outperforms the rest of the methods,but different scenarios, array configurations, a priori <strong>in</strong>formation, and other factors havea great impact on the performance of the explored schemes.For further research, some <strong>in</strong>terest<strong>in</strong>g situations are worth consider<strong>in</strong>g. 1) Performanceimprovement via MMSE beamform<strong>in</strong>g comes at the expense of <strong>in</strong>creased implementationcomplexity due to multiple MFs. If M matched filters were used also <strong>in</strong>the delay-angle method, then parallel <strong>acquisition</strong> would be used to decrease its T MA .This type of algorithm comparison, where the complexities of receivers are equal, isworth <strong>in</strong>vestigat<strong>in</strong>g. 2) The MMSE beamformer has an advantage that it does not needarray calibration, which makes its implementation easier. Investigation of the effectsof nonidealities, like amplitude and phase imbalances, on code <strong>acquisition</strong> is also an<strong>in</strong>terest<strong>in</strong>g topic. 3) S<strong>in</strong>ce it was concluded that there is not a s<strong>in</strong>gle approach whichgives the best performance <strong>in</strong> all scenarios, it would be <strong>in</strong>terest<strong>in</strong>g to research adaptivemethods that accord<strong>in</strong>g to some previous or estimated <strong>in</strong>formation select the bestconfiguration or approach for <strong>acquisition</strong>. 4) Interference cancellation and other code<strong>acquisition</strong> methods were <strong>in</strong>vestigated ma<strong>in</strong>ly <strong>in</strong> the channel where there are no multipathcomponents of signals. Performances of the methods under different types ofmultipath channels are also worth consider<strong>in</strong>g. 5) It would be <strong>in</strong>terest<strong>in</strong>g to <strong>in</strong>vestigatewhat is a powerful method <strong>in</strong> a scenario where all DOAs are unknown and an <strong>in</strong>terfer<strong>in</strong>gsignal is arriv<strong>in</strong>g from the same <strong>direct</strong>ion as the desired signal. The delay-angle methodprobably works also <strong>in</strong> this particular scenario, if scann<strong>in</strong>g is performed us<strong>in</strong>g e.g., thespace-time MVDR algorithm. However, it would be <strong>in</strong>terest<strong>in</strong>g to know if it is possibleto develop space-time versions of the whiten<strong>in</strong>g filter and MMSE beamformers, andwhat is their performance.77
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ACTA UNIVERSITATIS OULUENSISSERIES C TECHNICA302. Teräs, Jukka (2008) Regional science-based clusters. A case study of threeEuropean concentrations303. Lahti, Markku (2008) Gravure offset pr<strong>in</strong>t<strong>in</strong>g for fabrication of electronic devicesand <strong>in</strong>tegrated components <strong>in</strong> LTCC modules304. Popov, Alexey (2008) TiO 2 nanoparticles as UV protectors <strong>in</strong> sk<strong>in</strong>305. Illika<strong>in</strong>en, Mirja (2008) Mechanisms of thermomechanical pulp ref<strong>in</strong><strong>in</strong>g306. Borkowski, Maciej (2008) Digital Δ-Σ Modulation. Variable modulus and tonalbehaviour <strong>in</strong> a fixed-po<strong>in</strong>t digital environment307. Kuismanen, Kimmo (2008) Climate-conscious architecture—design and w<strong>in</strong>dtest<strong>in</strong>g method for climates <strong>in</strong> change308. Kangasvieri, Tero (2008) Surface-mountable LTCC-SiP module approach forreliable RF and millimetre-wave packag<strong>in</strong>g309. Metsär<strong>in</strong>ta, Maija-Leena (2008) S<strong>in</strong>kkivälkkeen leijukerrospasutuksen stabiilisuus310. Prokkola, Jarmo (2008) Enhanc<strong>in</strong>g the performance of ad hoc network<strong>in</strong>g bylower layer design311. Löytynoja, Mikko (2008) Digital rights management of audio distribution <strong>in</strong> mobilenetworks312. El Harouny, Elisa (2008) Historiall<strong>in</strong>en puukaupunki suojelukohteena jael<strong>in</strong>ympäristönä. Esimerkke<strong>in</strong>ä Vanha Porvoo ja Vanha Raahe. Osa 1312. El Harouny, Elisa (2008) Historiall<strong>in</strong>en puukaupunki suojelukohteena jael<strong>in</strong>ympäristönä. Esimerkke<strong>in</strong>ä Vanha Porvoo ja Vanha Raahe. Osa 2313. Hannuksela, Jari (2008) Camera based motion estimation and recognition forhuman-computer <strong>in</strong>teraction314. Niem<strong>in</strong>en, Timo (2009) Detection of harmful microbes and their metaboliteswith novel methods <strong>in</strong> the agri-food production cha<strong>in</strong>315. Marjala, Pauli<strong>in</strong>a (2009) Työhyv<strong>in</strong>vo<strong>in</strong>n<strong>in</strong> kokemukset kertomuksellis<strong>in</strong>aprosesse<strong>in</strong>a–narratiiv<strong>in</strong>en arvio<strong>in</strong>titutkimus316. Ahola, Juha (2009) Reaction k<strong>in</strong>etics and reactor modell<strong>in</strong>g <strong>in</strong> the design ofcatalytic reactors for automotive exhaust gas abatement317. Koskimäki, Heli (2009) Utiliz<strong>in</strong>g similarity <strong>in</strong>formation <strong>in</strong> <strong>in</strong>dustrial applicationsBook orders:OULU UNIVERSITY PRESSP.O. Box 8200, FI-90014University of <strong>Oulu</strong>, F<strong>in</strong>landDistributed byOULU UNIVERSITY LIBRARYP.O. Box 7500, FI-90014University of <strong>Oulu</strong>, F<strong>in</strong>land
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