On Capacity of Large-Scale MIMO Multiple Access Channels with Distributed Sets of Correlated Antennas

TL;DR

This paper derives a deterministic equivalent for the ergodic sum rate and proposes an algorithm to evaluate capacity-achieving input covariance matrices in large-scale MIMO systems with distributed correlated antennas, enhancing understanding of system capacity.

Contribution

It introduces a novel deterministic equivalent for ergodic sum rate and an algorithm for capacity optimization in large-scale distributed MIMO channels, based on advanced random matrix theory techniques.

Findings

Deterministic equivalent of ergodic sum rate is accurate for realistic system sizes.

The results are invariant to various fading distributions.

The proposed methods simplify capacity evaluation in large MIMO systems.

Abstract

In this paper, a deterministic equivalent of ergodic sum rate and an algorithm for evaluating the capacity-achieving input covariance matrices for the uplink large-scale multiple-input multiple-output (MIMO) antenna channels are proposed. We consider a large-scale MIMO system consisting of multiple users and one base station with several distributed antenna sets. Each link between a user and an antenna set forms a two-sided spatially correlated MIMO channel with line-of-sight (LOS) components. Our derivations are based on novel techniques from large dimensional random matrix theory (RMT) under the assumption that the numbers of antennas at the terminals approach to infinity with a fixed ratio. The deterministic equivalent results (the deterministic equivalent of ergodic sum rate and the capacity-achieving input covariance matrices) are easy to compute and shown to be accurate for realistic system dimensions. In addition, they are shown to be invariant to several types of fading distribution.