Feedback Strategies for Multiantenna Multiuser Systems

In this chapter, we focus on the different feedback strategies for the case of a multiuser wireless communication system with multiple transmitter and receiver antennas. Firstly, we analyze the capacity of the multiuser MIMO system with single receive antenna for uplink and downlink by assuming that the full channel state information (CSI) is available at the transmitter. Secondly, we examine the precoding and user selection algorithms for MIMO multiuser systems with one and multiple receive antennas in flat fading and frequency selective wireless channels. Since optimal precoding using dirty paper coding has a prohibitively high computational complexity due to the associated encoding process, it is a great practical interest to design MIMO multiuser systems with low complexity and a minimum CSI requirement at the transmitter side. One suboptimal approach is to apply linear precoding schemes, such as zero forcing beamforming (ZF-BF) or minimum mean square error criterion. Multiuser MIMO wireless communication with ZF-BF requires a brute-force exhaustive search over all possible user sets and the complexity of an exhaustive search is prohibitive when the number of users is large. In order to decrease the complexity of this search, several suboptimal user scheduling algorithms have been designed. Generally, these algorithms fall into two categories: Capacity-based and Frobenius norm-based algorithm. Lastly, we show the effect of reduced and limited feedback information including user selection at the receiver side and quantization for both single carrier and multicarrier transmissions. We perform user selection at the user side since the users having a poor channel (low norm or/and interference) should not take part in the user selection algorithm, nor feedback their channel information. By using a self-discrimination criterion at the receiver side, it is possible to reduce the feedback load and the complexity of the user selection algorithm at base station. We show different user selection criteria and quantization strategies to reduce feedback load for both single and multicarrier communication systems.

[1]  James Sungjin Kim,et al.  On the Performance of Multiuser MIMO Systems in WCDMA/HSDPA: Beamforming, Feedback and User Diversity , 2006, IEICE Trans. Commun..

[2]  Berna Özbek,et al.  Reduced Feedback Designs for SDMA-OFDMA Systems , 2009, ICC.

[3]  Andrea J. Goldsmith,et al.  Dirty-paper coding versus TDMA for MIMO Broadcast channels , 2005, IEEE Transactions on Information Theory.

[4]  Rick S. Blum,et al.  Multiuser diversity for a dirty paper approach , 2003, IEEE Communications Letters.

[5]  Syed Ali Jafar,et al.  Interference Alignment and Degrees of Freedom of the $K$-User Interference Channel , 2008, IEEE Transactions on Information Theory.

[6]  Ross D. Murch,et al.  A transmit preprocessing technique for multiuser MIMO systems using a decomposition approach , 2004, IEEE Transactions on Wireless Communications.

[7]  A. Lee Swindlehurst,et al.  A vector-perturbation technique for near-capacity multiantenna multiuser communication-part II: perturbation , 2005, IEEE Transactions on Communications.

[8]  John M. Cioffi,et al.  Weighted Sum-Rate Maximization Using Weighted MMSE for MIMO-BC Beamforming Design , 2008, 2009 IEEE International Conference on Communications.

[9]  Josef A. Nossek,et al.  Quantized CDI Based Tomlinson Harashima Precoding for Broadcast Channels , 2011, 2011 IEEE International Conference on Communications (ICC).

[10]  David Tse,et al.  Opportunistic beamforming using dumb antennas , 2002, IEEE Trans. Inf. Theory.

[11]  Nihar Jindal,et al.  MIMO broadcast channels with finite rate feedback , 2006, GLOBECOM '05. IEEE Global Telecommunications Conference, 2005..

[12]  Björn E. Ottersten,et al.  Opportunistic Beamforming and Scheduling for OFDMA Systems , 2007, IEEE Transactions on Communications.

[13]  Andrea J. Goldsmith,et al.  On the optimality of multiantenna broadcast scheduling using zero-forcing beamforming , 2006, IEEE Journal on Selected Areas in Communications.

[14]  Elza Erkip,et al.  On beamforming with finite rate feedback in multiple-antenna systems , 2003, IEEE Trans. Inf. Theory.

[15]  Kenneth W. Shum,et al.  Convergence of Iterative Waterfilling Algorithm for Gaussian Interference Channels , 2007, IEEE Journal on Selected Areas in Communications.

[16]  Il-Min Kim,et al.  Optimum opportunistic beamforming based on multiple weighting vectors , 2005, IEEE International Conference on Communications, 2005. ICC 2005. 2005.

[17]  Jungwoo Lee,et al.  Multiuser MIMO User Selection Based on Chordal Distance , 2012, IEEE Transactions on Communications.

[18]  Babak Hassibi,et al.  On the capacity of MIMO broadcast channels with partial side information , 2005, IEEE Transactions on Information Theory.

[19]  H. Miyakawa,et al.  Matched-Transmission Technique for Channels With Intersymbol Interference , 1972, IEEE Trans. Commun..

[20]  Wei Yu,et al.  Trellis and convolutional precoding for transmitter-based interference presubtraction , 2005, IEEE Transactions on Communications.

[21]  Shidong Zhou,et al.  Multiuser MIMO downlink precoder design based on the maximal SJNR criterion , 2005, GLOBECOM '05. IEEE Global Telecommunications Conference, 2005..

[22]  Jeffrey G. Andrews,et al.  Performance of Orthogonal Beamforming for SDMA With Limited Feedback , 2006, IEEE Transactions on Vehicular Technology.

[23]  Mérouane Debbah,et al.  Massive MIMO: How many antennas do we need? , 2011, 2011 49th Annual Allerton Conference on Communication, Control, and Computing (Allerton).

[24]  Erik G. Larsson,et al.  Scaling Up MIMO: Opportunities and Challenges with Very Large Arrays , 2012, IEEE Signal Process. Mag..

[25]  Wei Yu,et al.  Sum capacity of Gaussian vector broadcast channels , 2004, IEEE Transactions on Information Theory.

[26]  John M. Cioffi,et al.  Multiuser Diversity in a MIMO System With Opportunistic Feedback , 2009, IEEE Transactions on Vehicular Technology.

[27]  Mohamed-Slim Alouini,et al.  How much feedback is multi-user diversity really worth? , 2004, 2004 IEEE International Conference on Communications (IEEE Cat. No.04CH37577).

[28]  M. Tomlinson.  New automatic equaliser employing modulo arithmetic , 1971 .

[29]  Didier Le Ruyet,et al.  Feedback channel designs for fair scheduling in MISO–OFDMA systems , 2012, EURASIP J. Wirel. Commun. Netw..

[30]  Babak Hassibi,et al.  A Comparison of Time-Sharing, DPC, and Beamforming for MIMO Broadcast Channels With Many Users , 2007, IEEE Transactions on Communications.

[31]  Jeffrey G. Andrews,et al.  WLCp1-16: Capacity of Opportunistic Space Division Multiple Access with Beam Selection , 2006, IEEE Globecom 2006.

[32]  Il-Min Kim,et al.  Opportunistic beamforming based on multiple weighting vectors , 2005, IEEE Transactions on Wireless Communications.

[33]  Kiho Kim,et al.  A random beamforming technique in MIMO systems exploiting multiuser diversity , 2003, IEEE J. Sel. Areas Commun..

[34]  David Tse,et al.  Sum capacity of the vector Gaussian broadcast channel and uplink-downlink duality , 2003, IEEE Trans. Inf. Theory.

[35]  N.D. Sidiropoulos,et al.  On downlink beamforming with greedy user selection: performance analysis and a simple new algorithm , 2005, IEEE Transactions on Signal Processing.

[36]  Andrea J. Goldsmith,et al.  On the duality of Gaussian multiple-access and broadcast channels , 2002, IEEE Transactions on Information Theory.

[37]  Erik G. Larsson,et al.  Uplink power efficiency of multiuser MIMO with very large antenna arrays , 2011, 2011 49th Annual Allerton Conference on Communication, Control, and Computing (Allerton).

[38]  Thomas L. Marzetta,et al.  Noncooperative Cellular Wireless with Unlimited Numbers of Base Station Antennas , 2010, IEEE Transactions on Wireless Communications.

[39]  Jeffrey G. Andrews,et al.  Low Complexity User Selection Algorithms for Multiuser MIMO Systems with Block Diagonalization , 2006, Conference Record of the Thirty-Ninth Asilomar Conference onSignals, Systems and Computers, 2005..

[40]  Matthew R. McKay,et al.  Eigen-Based Transceivers for the MIMO Broadcast Channel With Semi-Orthogonal User Selection , 2010, IEEE Transactions on Signal Processing.

[41]  Amir K. Khandani,et al.  Communication Over MIMO X Channels: Interference Alignment, Decomposition, and Performance Analysis , 2008, IEEE Transactions on Information Theory.

[42]  Wei Yu,et al.  The optimality of beamforming in uplink multiuser wireless systems , 2004, IEEE Transactions on Wireless Communications.

[43]  Jeffrey G. Andrews,et al.  MISO Broadcast Channels with Delayed Finite-Rate Feedback: Predict or Observe? , 2011, IEEE Transactions on Wireless Communications.

[44]  N. Jindal,et al.  Dirty Paper Coding vs. Linear Precoding for MIMO Broadcast Channels , 2006, 2006 Fortieth Asilomar Conference on Signals, Systems and Computers.

[45]  Didier Le Ruyet,et al.  Semi-orthogonal user selection for MISO systems with quantized feedback , 2008, 2008 International ITG Workshop on Smart Antennas.

[46]  Shlomo Shamai,et al.  Nested linear/Lattice codes for structured multiterminal binning , 2002, IEEE Trans. Inf. Theory.

[47]  G. Wornell,et al.  Finding NEMO: near mutually orthogonal sets and applications to MIMO broadcast scheduling , 2005, 2005 International Conference on Wireless Networks, Communications and Mobile Computing.

[48]  Paolo Giaccone,et al.  Asymptotic Performance Limits of Switches With Buffered Crossbars Supporting Multicast Traffic , 2008, IEEE Transactions on Information Theory.

[49]  Shlomo Shamai,et al.  On the achievable throughput of a multiantenna Gaussian broadcast channel , 2003, IEEE Transactions on Information Theory.

[50]  Martin Haardt,et al.  Zero-forcing methods for downlink spatial multiplexing in multiuser MIMO channels , 2004, IEEE Transactions on Signal Processing.

[51]  Max H. M. Costa,et al.  Writing on dirty paper , 1983, IEEE Trans. Inf. Theory.

[52]  T.L. Marzetta,et al.  How Much Training is Required for Multiuser Mimo? , 2006, 2006 Fortieth Asilomar Conference on Signals, Systems and Computers.

[53]  A. Lee Swindlehurst,et al.  A vector-perturbation technique for near-capacity multiantenna multiuser communication-part I: channel inversion and regularization , 2005, IEEE Transactions on Communications.

[54]  Andrea J. Goldsmith,et al.  Duality, achievable rates, and sum-rate capacity of Gaussian MIMO broadcast channels , 2003, IEEE Trans. Inf. Theory.

[55]  Yeheskel Bar-Ness,et al.  Sum-Rate of MIMO Broadcast Channels with One Bit Feedback , 2006, 2006 IEEE International Symposium on Information Theory.