A Low-Complexity Design of Linear Precoding for MIMO Channels with Finite-Alphabet Inputs

This paper investigates linear precoding scheme that maximizes mutual information for multiple-input multiple-output (MIMO) channels with finite-alphabet inputs. In contrast with recent studies, optimizing mutual information directly with extensive computational burden, this work proposes a low-complexity and high-performance design. It derives a lower bound that demands low computational effort and approximates, with a constant shift, the mutual information for various settings. Based on this bound, the precoding problem is solved efficiently. Numerical examples show the efficacy of this method for constant and fading MIMO channels. Compared to its conventional counterparts, the proposed method reduces the computational complexity without performance loss.

[1]  Georgios B. Giannakis,et al.  Space-time diversity systems based on linear constellation precoding , 2003, IEEE Trans. Wirel. Commun..

[2]  Stephen P. Boyd,et al.  Convex Optimization , 2004, Algorithms and Theory of Computation Handbook.

[3]  Andrea J. Goldsmith,et al.  Capacity limits of MIMO channels , 2003, IEEE J. Sel. Areas Commun..

[4]  Miguel R. D. Rodrigues,et al.  MIMO Gaussian Channels With Arbitrary Inputs: Optimal Precoding and Power Allocation , 2010, IEEE Transactions on Information Theory.

[5]  A. Paulraj,et al.  MIMO Wireless Linear Precoding , 2007, IEEE Signal Processing Magazine.

[6]  Zhi Ding,et al.  Globally Optimal Linear Precoders for Finite Alphabet Signals Over Complex Vector Gaussian Channels , 2011, IEEE Transactions on Signal Processing.

[7]  B. Sundar Rajan,et al.  On Two-User Gaussian Multiple Access Channels With Finite Input Constellations , 2011, IEEE Transactions on Information Theory.

[8]  Shlomo Shamai,et al.  Mutual information and minimum mean-square error in Gaussian channels , 2004, IEEE Transactions on Information Theory.

[9]  Joseph Lipka,et al.  A Table of Integrals , 2010 .

[10]  Antonia Maria Tulino,et al.  Optimum power allocation for parallel Gaussian channels with arbitrary input distributions , 2006, IEEE Transactions on Information Theory.