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.

2 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.; 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.

3 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.

4 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.

5 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.

6 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.

7 (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.; Assaad, M.; Debbah, M., Coordinated Multicell Beamforming forMassive MIMO: A Random Matrix Approach , IEEE Tran.

8 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.

9 (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.

10 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: 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.