On Spectral Efficiency of Vector Precoding for Gaussian MIMO Broadcast Channels

The spectral efficiency of practically oriented vector precoding schemes for the Gaussian multiple-input multiple-output (MIMO) broadcast channel is analyzed in the large system limit. Considering discrete complex input alphabets, the transmitter is assumed to comprise a linear front-end combined with nonlinear precoding, that minimizes the transmit energy penalty imposed by the linear front-end. The energy penalty is minimized by relaxing the input alphabet to a larger alphabet set prior to precoding. The so-called "replica method" of statistical physics is employed to derive the limiting empirical distribution of the precoder's output, as well as the limiting energy penalty. Particularizing to a "zero-forcing" (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 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. A comparative spectral efficiency analysis of two illustrative examples reveals significant performance gains compared to linear ZF precoding in the medium to high Et/No region. In particular, we demonstrate that convex extended alphabets, amenable to efficient energy minimization algorithms, provide an attractive alternative to alphabets based on the discrete Gaussian integer lattice, for which the energy minimization problem is NP-hard.

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

[2]  Helmut Bölcskei,et al.  Space-Time Wireless Systems: From Array Processing to MIMO Communications , 2008 .

[3]  Shlomo Shamai,et al.  Space-Time Wireless Systems: On information-theoretic aspects of MIMO broadcast channels , 2006 .

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

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

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

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

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

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

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

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

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

[13]  Convex Precoding for Vector Channels in High Dimensions , 2008, 2008 IEEE International Zurich Seminar on Communications.

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

[15]  G. Caire,et al.  Precoding Schemes for the MIMO-GBC , 2006, 2006 International Zurich Seminar on Communications.

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

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