Beamforming Techniques in Wireless …

1 Beamforming Techniques in WirelessCommunicationsJaved AkhtarSupervisor:Dr. Ketan RajawatDepartment of Electrical EngineeringIndian Institute of Technology, KanpurKanpur, Uttar PradeshBeamforming Techniques in Wireless Communications1 / 51 OutlineBeamforming: Introduction & OverviewTransmit & Receive Beamforming :- ExamplesCommon Beamforming TechniquesBeamformingIn MIMO SystemsIn Massive MIMO SystemsIn MIMO-OFDM SystemsFor Interference Mitigation in Multi-antenna, Multi-carrier SystemsIn Wireless Sensor NetworksFuture WorkBeamforming Techniques in Wireless Communications2 / 51 BeamformingDirectional transmission/reception to optimize a design criterion [1]Objective:Design transmit & receive beamformers to nullify theinterference and enhance system performanceCan be used at both transmitting and receiving endsApplication in different areas of Wireless communications:Multiple-Input and Multiple-output(MIMO)Massive MIMO(m-MIMO)Interference mitigationWireless sensor Networks(WSN)[1] Mietzner, J.

2 ; et al., Multiple-antenna Techniques for Wireless communications - acomprehensive literature survey, in Communications Surveys & Tutorials, 2009 Beamforming Techniques in Wireless Communications3 / 51 System Model: An OverviewPWChannel(H)X=Psyr=WysTransmit BeamformerReceive BeamformerTxRxThe general model for a MIMO Wireless communication is given asy=Hx+n;H CNR NT(1)=HPs+n(2)x=Psis the precoded andr=Wyis the filtered outputBeamforming Techniques in Wireless Communications4 / 51 Transmit & Receive Beamforming :- ExamplesBeamforming Techniques in Wireless Communications5 / 51 Transmit and Receive Beamforming - ExampleConsider singular value decomposition(SVD) ofHH=U VH(3)where is a diagonal matrix with entries i(singular values)ChoosingP=VandW=UHin eq. (2) givesr= s+UHn(4)= s+ n(5)Then, one data stream per singular value can be transmitted asri= isi+ ni; 1 i min{NR,NT}(6)[2] E.

3 Telatar, Capacity of multiantenna Gaussian channels, European Transactions onTelecommunications , 1999 Beamforming Techniques in Wireless Communications6 / 51 Precoder: Transmit Beamforming - ExampleMultiple Input Single Output(MISO) system[3]y=hHfs+n(7)Here,P=fandW=IAntenn a Selection: Send data on the antenna that maximizes thereceive SNR=|hHf|2mopt= arg max1 m NT|h(m)|2(8)Transmit Beamforming vector is restricted to rank one covariancematrixOptimal selected antenna can be fedback to the transmitter usingdlog2(NT)ebits[3] D. Love, R. Heath, et al., An overview of limited feedback in Wireless communicationsystems , IEEE Journal on Selected Areas in Communications, Techniques in Wireless Communications7 / 51 Common Beamforming TechniquesBeamforming Techniques in Wireless Communications8 / 51 MRC: Maximal Ratio CombiningAll received signals are coherently combined at the receiver [5]Here,W=w(vector withNRelements) andP=Ir=wHy=wHhx+wHn(9)Maximizes the output SNR for the intended user =|wHh|2E|wHn|2=|wHh|2 2E|wHw|(10)By Cauchy-Schwartz inequality it is found that the SNR ismaximized when,w h[4] Tse, David, and Pramod Viswanath,Fundamentals of Wireless communication , Cambridgeuniversity press, 2005 Beamforming Techniques in Wireless Communications9 / 51ZF: Zero ForcingPrecoding.

4 MISO system with (K)-user is considered in [6]Received signal atkthuser user is given asyk=hHkx+nk;k= 1,2, ,K(11)where,x= Ki=1siwiis theNT 1 transmitted symbolswiis the linear precoder, orthogonal to all other user channel vectorsHence,yk=hHk Ki=1wisi+nk=hHkwksk+nk;k= 1,2, ,K(12)[5] Peel, et. al., A vector-perturbation technique for near-capacity multiantenna multiusercommunication-part I: channel inversion and regularization, IEEE Tran. on Comm., 2005 Beamforming Techniques in Wireless Communications10 / 51ZF: Zero ForcingEqualization:Considering the MIMO signal model given in eq. (1)To decouple the detection of each symbol at the receiverW=H so,r=H y=x+H n(13)where,H is the pseudo inverse given as (HHH) 1HH[6] Tse, David, and Pramod Viswanath,Fundamentals of Wireless communication , Cambridgeuniversity press, 2005 Beamforming Techniques in Wireless Communications11 / 51 State of the ArtMulti-cell MIMO Networks [a],[b]Cognitive Radio Networks [c],Massive-MIMO Networks [a]Dirty Paper Coding (DPC) based algorithms[d][a] Lakshminarayana, S.

5 ; Assaad, M.; Debbah, M., Coordinated Multicell Beamforming forMassive MIMO: A Random Matrix Approach , IEEE Tran. on Inform. Th., 2015[b]Venkategowda, ; et al., MVDR-Based Multicell Cooperative Beamforming Techniques forUnicast/Multicast MIMO Networks With Perfect/Imperfect CSI , Tran. on Veh. Tech., 2015[c]Afana, A.; et al., Distributed Beamforming for Two-Way DF Relay Cognitive Networks UnderPrimarySecondary Mutual Interference , Tran. on Veh. Tech., 2015[d] N. Jindal and A. Goldsmith,Dirty-paper coding versus TDMA for MIMO broadcast channels,IEEE Trans. Inf. Theory, 2010 Beamforming Techniques in Wireless Communications12 / 51 Beamforming in MIMOB eamforming Techniques in Wireless Communications13 / 51 Beamforming in MIMOIn [7], transmission of data symbols subject to (possibly different)QoSconstraints is consideredSystem model is same as in eq.

6 (1)Optimum Receiver: for a given transmit matrixPminWTr[( x x)( x x)H](14)where x=Wxand the optimalWis the linear minimum MSE(LMMSE) filter[8][7] D. P. Palomar, et al.,Optimum linear joint transmit-receive processing for MIMO channels withQoS constraints,IEEE Trans. Signal Process., 2004[8] Palomar, and Jiang, Y., MIMO transceiver design via majorization theory. Foundationsand trends in communications and information theory , 2006 Beamforming Techniques in Wireless Communications14 / 51 Beamforming in MIMO [7] Precoder:minPTr(PPH)(15) [(I+PHRHP) 1]ii i,1 i L(16)where,Lis the number of established links andRH=HHR 1nHAbove problem is nonconvex inPMajorization theory is used to reformulate it as a simple convexoptimization problemContribution:A practical and efficient multilevel water-filling algorithm is proposedA simple robust design under channel estimation errors is alsoproposedBeamforming Techniques in Wireless Communications15 / 51 Beamforming in MIMO.

7 Robust FrameworkIn [9], the design of linear transceivers with robustness for imperfectCSI is consideredThe MIMO channel matrix distribution is modeled asH=H+ (RRxH)1/2G(RTxH)(1/2)H(17)whereGis the unknown part in the fading estimateHis the channel mean & covariance matrixRH= (RRxH) (RTxH)(18)[9] Xi Zhang; et al., Statistically Robust Design of Linear MIMO Transceivers, in TSP, 2008 Beamforming Techniques in Wireless Communications16 / 51 Beamforming in MIMO [9] Problem:minW,PFo{Tr[E{( x x)( x x)H}]}(19) Tr[PP ] pmax(20)where,Fois an arbitrary increasing function of the average MSECase1: Imperfect CSIR & CSITA closed-form expression for the average MSE matrix isE{E(W,P)}= [(WHHP Il)(PHHHW Il) +WHR nW](21)where,R n=Rn+ Tr[PPH(RTxH)T]RRxHThe optimal receiverWis similar to the Wiener filterThe optimal design of the transmitterPis derived forRTxH=INTB eamforming Techniques in Wireless Communications17 / 51 Beamforming in MIMO [9].

8 Perfect CSIR & imperfect CSITThe optimal receiverWis exactly the same as for perfect CSI caseA robust transmitter design is proposed based on a tight lower boundofE{E}Cost function of the design problem (19) is also lower-boundedBeamforming Techniques in Wireless Communications18 / 51 Beamforming in Massive MIMOB eamforming Techniques in Wireless Communications19 / 51 Beamforming in Massive MIMOM assive MIMO employs hundreds of antennas to enable a largebeamforming gainIn [10], quantized versions of the channel estimates are used forconjugate beamformingDesirable to use A/D and D/A converters with resolutions as coarseas possibleM-antenna base station,Kautonomous single antenna terminalswith Time Divison Duplexing(TDD) operation is consideredThe minimum mean-square error (MMSE) estimate forHis H= u u1+ u uywhere uis coherence slot for up-link pilots uis a measure of the expected SNR of the up-link[10] Hong Yang.

9 Marzetta, , Quantized Beamforming in Massive MIMO , Annual Conferenceon Information Sciences and Systems (CISS), 2015 Beamforming Techniques in Wireless Communications20 / 51 Beamforming in Massive MIMO[10] Beamforming :x= HHq MK u u1+ u u(22)The down-link data channel is given byy= dHHx+n(23) dis a measure of the expected SNR of the down-link channelEffects of quantization :x= HH q cwhere, the constantc=E[Tr( H HH )] is the quantization scheme on the channel response estimate HOpen Problems:Consideration of multi-cell interference and pilot contaminationAnalysis and impact of multi-antenna receiverBeamforming Techniques in Wireless Communications21 / 51 Beamforming in Multi-Antenna &Multi-Carrier SystemBeamforming Techniques in Wireless Communications22 / 51 Beamforming in MIMO-OFDM SystemIn [11], robust transceiver designs with statistical channeluncertainties are consideredThe channel matrices on each subcarrier have the followingrelationshipHk= Hk+ Hk(24)where, Hk= 2kHw 2kis the estimation error variance of each channel elementHwis the random matrix whose elements are with zero meanand unit variance[11] Chengwen Xing; et al.

10 , Robust Transceiver Design for MIMO-OFDM Systems Based onCluster Water-Filling, in IEEE Communications Letters, 2013 Beamforming Techniques in Wireless Communications23 / 51 Beamforming in MIMO-OFDM Systems[11] weighted MSE optimization problem of transceiver design isformulated asminPkK k=1Tr{Wk[PHk HHkK 1Pk HkPk+I] 1}(25) KPk= [ 2e,kTr(PkPHk) + 2n]I(26)K k=1Tr(PkPHk) pmax(27)where,Wkis the weighting matrix in thekthsubcarrierA series of auxiliary variablesPkare introducedThus enabling optimization problem to be decoupled into a series ofsubproblemsCluster water-filling is proposed to solve the robust transceiverdesignBeamforming Techniques in Wireless Communications24 / 51 Beamforming for Interference MitigationBeamforming Techniques in Wireless Communications25 / 51 Interfernce AlignmentInterference Alignment (IA) concepts have been well explored in[12],[13]Interference may arise due to multiuser, multicell, Inter-BlockInterference(IBI), Inter-Symbol Interference (ISI)