PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Optimal Input Covariance for Multiple-Input Multiple-Output Gaussian Channels with Arbitrary Inputs
M Rodrigues, Fernando Perez-Cruz and S Verdu
In: IEEE Information Theory Workshop (ITW), 5-9 May 2008, Porto, Portugal.

Abstract

We investigate the input covariance that maximizes the mutual information of deterministic multiple-input multipleoutput (MIMO) Gaussian channels with arbitrary (not necessarily Gaussian) input distributions, by capitalizing on the relationship between the gradient of the mutual information and the minimum mean-squared error (MMSE) matrix. We show that the optimal input covariance satisfies a simple fixedpoint equation involving key system quantities, including the MMSE matrix. We also specialize the form of the optimal input covariance to the asymptotic regimes of low and high snr. We demonstrate that in the low-snr regime the optimal covariance fully correlates the inputs to better combat noise. In contrast, in the high-snr regime the optimal covariance is diagonal with diagonal elements obeying the generalized mercury/waterfilling power allocation policy. Numerical results illustrate that covariance optimization may lead to significant gains with respect to conventional strategies based on channel diagonalization followed by mercury/waterfilling or waterfilling power allocation, particularly in the regimes of medium and high snr.

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EPrint Type:Conference or Workshop Item (Talk)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
ID Code:4915
Deposited By:Fernando Perez-Cruz
Deposited On:24 March 2009