Quantized CSI-Based Tomlinson-Harashima Precoding in Multiuser MIMO Systems

This paper considers the implementation of Tomlinson-Harashima (TH) precoding for multiuser MIMO systems based on quantized channel state information (CSI) at the transmitter side. Compared with the results in [1], our scheme applies to more general system setting where the number of users in the system can be less than or equal to the number of transmit antennas. We also study the achievable average sum rate of the proposed quantized CSI-based TH precoding scheme. The expressions of the upper bounds on both the average sum rate of the systems with quantized CSI and the mean loss in average sum rate due to CSI quantization are derived. We also present some numerical results. The results show that the nonlinear TH precoding can achieve much better performance than that of linear zero-forcing precoding for both perfect CSI and quantized CSI cases. In addition, our derived upper bound on the mean rate loss for TH precoding converges to the true rate loss faster than that of zero-forcing precoding obtained in [2] as the number of feedback bits becomes large. Both the analytical and numerical results show that nonlinear precoding suffers from imperfect CSI more than linear precoding does.

[1]  Robert W. Heath,et al.  Adaptive Limited Feedback for Sum-Rate Maximizing Beamforming in Cooperative Multicell Systems , 2009, IEEE Transactions on Signal Processing.

[2]  Gerard J. Foschini,et al.  Layered space-time architecture for wireless communication in a fading environment when using multi-element antennas , 1996, Bell Labs Technical Journal.

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

[4]  D. Kershaw.  Some extensions of W. Gautschi’s inequalities for the gamma function , 1983 .

[5]  Golub Gene H. Et.Al.  Matrix Computations, 3rd Edition , 2007 .

[6]  Mohamed Oussama Damen,et al.  Lattice code decoder for space-time codes , 2000, IEEE Communications Letters.

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

[8]  Uri Erez,et al.  Achieving 1/2 log (1+SNR) on the AWGN channel with lattice encoding and decoding , 2004, IEEE Transactions on Information Theory.

[9]  Robert H. Halstead,et al.  Matrix Computations , 2011, Encyclopedia of Parallel Computing.

[10]  Nihar Jindal.  MIMO broadcast channels with finite rate feedback , 2005, GLOBECOM.

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

[12]  Richard D. Wesel,et al.  Achievable Rates for Tomlinson-Harashima Precoding , 1998, IEEE Trans. Inf. Theory.

[13]  Reinaldo A. Valenzuela,et al.  V-BLAST: an architecture for realizing very high data rates over the rich-scattering wireless channel , 1998, 1998 URSI International Symposium on Signals, Systems, and Electronics. Conference Proceedings (Cat. No.98EX167).

[14]  Andrea J. Goldsmith,et al.  Multi-Antenna Downlink Channels with Limited Feedback and User Selection , 2007, IEEE Journal on Selected Areas in Communications.

[15]  Shlomo Shamai,et al.  The Capacity Region of the Gaussian Multiple-Input Multiple-Output Broadcast Channel , 2006, IEEE Transactions on Information Theory.

[16]  Robert W. Heath,et al.  Adaptive Bit Partitioning for Multicell Intercell Interference Nulling With Delayed Limited Feedback , 2011, IEEE Transactions on Signal Processing.

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

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

[19]  J. Yang,et al.  Joint transmitter-receiver optimization for multi-input multi-output systems with decision feedback , 1994, IEEE Trans. Inf. Theory.

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

[21]  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.

[22]  David James Love,et al.  On the performance of random vector quantization limited feedback beamforming in a MISO system , 2007, IEEE Transactions on Wireless Communications.

[23]  Volker Jungnickel,et al.  Performance of MIMO systems with channel inversion , 2002, Vehicular Technology Conference. IEEE 55th Vehicular Technology Conference. VTC Spring 2002 (Cat. No.02CH37367).

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

[25]  Josef A. Nossek,et al.  Transmit Wiener filter for the downlink of TDDDS-CDMA systems , 2002, IEEE Seventh International Symposium on Spread Spectrum Techniques and Applications,.

[26]  Robert F. H. Fischer,et al.  Precoding and Signal Shaping for Digital Transmission , 2002 .

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

[28]  Emre Telatar,et al.  Capacity of Multi-antenna Gaussian Channels , 1999, Eur. Trans. Telecommun..

[29]  Antonio A. D'Amico,et al.  Tomlinson-Harashima Precoding in MIMO Systems: A Unified Approach to Transceiver Optimization Based on Multiplicative Schur-Convexity , 2008, IEEE Transactions on Signal Processing.

[30]  Robert F. H. Fischer,et al.  Precoding in multiantenna and multiuser communications , 2004, IEEE Transactions on Wireless Communications.