On replica symmetry breaking in vector precoding for the Gaussian MIMO broadcast channel

The so-called ldquoreplica methodrdquo of statistical physics is employed for the large system analysis of vector precoding for the Gaussian multiple-input multiple-output (MIMO) broadcast channel. Focusing on discrete complex input alphabets, the transmitter is assumed to comprise a linear front-end combined with nonlinear precoding, that minimizes the front-end imposed transmit energy penalty. The energy penalty is minimized by relaxing the input alphabet to a larger alphabet set prior to precoding. The limiting empirical distribution of the precoder's output, as well as the limiting energy penalty, are derived while harnessing what is referred to as the first order replica symmetry breaking (1RSB) ansatz. Particularizing to a ldquozero-forcingrdquo (ZF) linear front-end, and non-cooperative users, a decoupling result is derived according to which the channel observed by each of the individual receivers can be effectively characterized by the Markov chain u-x-y, where u is the channel input, x is the equivalent precoder output, and y is the channel output. An illustrative example is considered, based on discrete-lattice alphabet relaxation, for which the impact of replica symmetry breaking is demonstrated. A comparative spectral efficiency analysis reveals significant performance gains compared to linear ZF precoding in the medium to high Eb/N0 region. The performance vs. complexity tradeoff of the nonlinear precoding scheme is also shortly discussed.

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

[2]  H. Nishimori Statistical Physics of Spin Glasses and Information Processing , 2001 .

[3]  Shlomo Shamai,et al.  Multicell uplink spectral efficiency of coded DS-CDMA with random signatures , 2001, IEEE J. Sel. Areas Commun..

[4]  David Tse,et al.  Asymptotically optimal water-filling in vector multiple-access channels , 2001, IEEE Trans. Inf. Theory.

[5]  Shlomo Shamai,et al.  The impact of frequency-flat fading on the spectral efficiency of CDMA , 2001, IEEE Trans. Inf. Theory.

[6]  西森 秀稔 Statistical physics of spin glasses and information processing : an introduction , 2001 .

[7]  Toshiyuki Tanaka,et al.  A statistical-mechanics approach to large-system analysis of CDMA multiuser detectors , 2002, IEEE Trans. Inf. Theory.

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

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

[10]  Antonia Maria Tulino,et al.  Random Matrix Theory and Wireless Communications , 2004, Found. Trends Commun. Inf. Theory.

[11]  Sergio Verdú,et al.  Randomly spread CDMA: asymptotics via statistical physics , 2005, IEEE Transactions on Information Theory.

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

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

[14]  M. Talagrand The parisi formula , 2006 .

[15]  Ralf R. Müller,et al.  Vector Precoding in High Dimensions: A Replica Analysis , 2007, 2007 IEEE International Symposium on Information Theory.

[16]  Amir K. Khandani,et al.  Communication Over MIMO Broadcast Channels Using Lattice-Basis Reduction , 2006, IEEE Transactions on Information Theory.

[17]  Robert W. Heath,et al.  A Lattice-Theoretic Analysis of Vector Perturbation for Multi-User MIMO Systems , 2008, 2008 IEEE International Conference on Communications.

[18]  B.M. Zaidel,et al.  On Spectral Efficiency of Vector Precoding for Gaussian MIMO Broadcast Channels , 2008, 2008 IEEE 10th International Symposium on Spread Spectrum Techniques and Applications.

[19]  Ralf R. Müller,et al.  Real vs. complex BPSK precoding for MIMO broadcast channels , 2008, PIMRC.