PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

MIMO Gaussian Channels with Arbitrary Inputs: Optimal Precoding and Power Allocation
Fernando Perez-Cruz, Miguel Rodrigues and Sergio Verdu
IEEE Transactions on Information Theory Volume 56, Number 3, pp. 1070-1084, 2010. ISSN 0018-9448

Abstract

In this paper, we investigate the linear precoding and power allocation policies that maximize the mutual information for general multiple-input-multiple-output (MIMO) Gaussian channels with arbitrary input distributions, by capitalizing on the relationship between mutual information and minimum mean-square error (MMSE). The optimal linear precoder satisfies a fixed-point equation as a function of the channel and the input constellation. For non-Gaussian inputs, a nondiagonal precoding matrix in general increases the information transmission rate, even for parallel noninteracting channels. Whenever precoding is precluded, the optimal power allocation policy also satisfies a fixed-point equation; we put forth a generalization of the mercury/waterfilling algorithm, previously proposed for parallel noninterfering channels, in which the mercury level accounts not only for the non-Gaussian input distributions, but also for the interference among inputs.

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EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
ID Code:7542
Deposited By:Fernando Perez-Cruz
Deposited On:17 March 2011