Statistical Eigenmode Transmission over Jointly-Correlated MIMO Channels

TL;DR

This paper analyzes MIMO eigenmode transmission using statistical channel information in a general jointly-correlated model, deriving capacity bounds and low-complexity power allocation algorithms with proven optimality and near-optimal performance.

Contribution

It introduces a closed-form capacity upper bound for jointly-correlated MIMO channels and develops an iterative water-filling algorithm for optimal power allocation based on channel statistics.

Findings

The capacity upper bound is tight and accurate.

The proposed power allocation algorithm converges and performs near-optimally.

The model generalizes many existing correlation models.

Abstract

We investigate MIMO eigenmode transmission using statistical channel state information at the transmitter. We consider a general jointly-correlated MIMO channel model, which does not require separable spatial correlations at the transmitter and receiver. For this model, we first derive a closed-form tight upper bound for the ergodic capacity, which reveals a simple and interesting relationship in terms of the matrix permanent of the eigenmode channel coupling matrix and embraces many existing results in the literature as special cases. Based on this closed-form and tractable upper bound expression, we then employ convex optimization techniques to develop low-complexity power allocation solutions involving only the channel statistics. Necessary and sufficient optimality conditions are derived, from which we develop an iterative water-filling algorithm with guaranteed convergence. Simulations demonstrate the tightness of the capacity upper bound and the near-optimal performance of the proposed low-complexity transmitter optimization approach.