A vector-perturbation technique for near-capacity multiantenna multiuser communication-part II: perturbation

Recent theoretical results describing the sum-capacity when using multiple antennas to communicate with multiple users in a known rich scattering environment have not yet been followed with practical transmission schemes that achieve this capacity. We introduce a simple encoding algorithm that achieves near-capacity at sum-rates of tens of bits/channel use. The algorithm is a variation on channel inversion that regularizes the inverse and uses a "sphere encoder" to perturb the data to reduce the energy of the transmitted signal. The paper is comprised of two parts. In this second part, we show that, after the regularization of the channel inverse introduced in the first part, a certain perturbation of the data using a "sphere encoder" can be chosen to further reduce the energy of the transmitted signal. The performance difference with and without this perturbation is shown to be dramatic. With the perturbation, we achieve excellent performance at all signal-to-noise ratios. The results of both uncoded and turbo-coded simulations are presented.

[1]  U. Fincke,et al.  Improved methods for calculating vectors of short length in a lattice , 1985 .

[2]  Robert F. H. Fischer,et al.  MIMO precoding for decentralized receivers , 2002, Proceedings IEEE International Symposium on Information Theory,.

[3]  László Lovász,et al.  Factoring polynomials with rational coefficients , 1982 .

[4]  Giuseppe Caire,et al.  On maximum-likelihood detection and the search for the closest lattice point , 2003, IEEE Trans. Inf. Theory.

[5]  Stephan ten Brink,et al.  Achieving near-capacity on a multiple-antenna channel , 2003, IEEE Trans. Commun..

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

[7]  Robert F. H. Fischer,et al.  Lattice-reduction-aided broadcast precoding , 2004, IEEE Transactions on Communications.

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

[9]  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.

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

[11]  J. Cioffi,et al.  Achievable rates for Tomlinson-Harashima precoding , 1995, Proceedings of 1995 IEEE International Symposium on Information Theory.

[12]  Robert F. H. Fischer,et al.  Precoding and loading for BLAST-like systems , 2003, IEEE International Conference on Communications, 2003. ICC '03..

[13]  G. Ginis,et al.  A multi-user precoding scheme achieving crosstalk cancellation with application to DSL systems , 2000, Conference Record of the Thirty-Fourth Asilomar Conference on Signals, Systems and Computers (Cat. No.00CH37154).

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

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

[16]  Ravi Kannan,et al.  Improved algorithms for integer programming and related lattice problems , 1983, STOC.

[17]  Uri Erez,et al.  Achieving SNR on the AWGN Channel With Lattice Encoding and Decoding , 2004 .

[18]  Alexander Vardy,et al.  Closest point search in lattices , 2002, IEEE Trans. Inf. Theory.

[19]  N. S. Barnett,et al.  Private communication , 1969 .

[20]  Hesham El Gamal,et al.  On the role of MMSE in lattice decoding : achieving the optimal diversity-vs-multiplexing tradeoff , 2003 .

[21]  R. Zamir,et al.  Lattice decoding can achieve 1/2 log(1+SNR) on the AWGN channel using nested codes , 2001, Proceedings. 2001 IEEE International Symposium on Information Theory (IEEE Cat. No.01CH37252).

[22]  Reinaldo A. Valenzuela,et al.  Simplified processing for high spectral efficiency wireless communication employing multi-element arrays , 1999, IEEE J. Sel. Areas Commun..

[23]  Wai Ho Mow,et al.  Universal lattice decoding: principle and recent advances , 2003, Wirel. Commun. Mob. Comput..

[24]  Babak Hassibi,et al.  On the expected complexity of integer least-squares problems , 2002, 2002 IEEE International Conference on Acoustics, Speech, and Signal Processing.

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

[26]  Bertrand M. Hochwald,et al.  Space-Time Multiple Access: Linear Growth in the Sum Rate , 2002 .

[27]  John M. Cioffi,et al.  Vectored transmission for digital subscriber line systems , 2002, IEEE J. Sel. Areas Commun..

[28]  Robert F. H. Fischer,et al.  Precoding in Multi-Antenna and Multi-User Communications , .

[29]  Shlomo Shamai,et al.  Capacity and lattice strategies for canceling known interference , 2005, IEEE Transactions on Information Theory.

[30]  Giuseppe Caire,et al.  Lattice coding and decoding achieve the optimal diversity-multiplexing tradeoff of MIMO channels , 2004, IEEE Transactions on Information Theory.

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

[32]  Uri Erez,et al.  Lattice Decoding Can Achieve on the AWGN Channel using Nested Codes , 2001 .

[33]  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.

[34]  A. Edelman Eigenvalues and condition numbers of random matrices , 1988 .

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