Distributed Dimension Reduction for Distributed Massive MIMO C-RAN with Finite Fronthaul Capacity

TL;DR

This paper introduces a decentralized lossy dimension reduction method for distributed Massive MIMO C-RAN systems to significantly decrease fronthaul traffic with minimal performance loss.

Contribution

It proposes a novel, decentralized linear dimension reduction technique based on a variant of the conditional Karhunen-Loeve transform for Massive MIMO C-RAN.

Findings

Reduces uplink and downlink fronthaul traffic substantially.

Maintains near-optimal MIMO performance with the proposed filters.

Enables decentralized computation of dimension reduction filters.

Abstract

The use of a large excess of service antennas brings a variety of performance benefits to distributed MIMO C-RAN, but the corresponding high fronthaul data loads can be problematic in practical systems with limited fronthaul capacity. In this work we propose the use of lossy dimension reduction, applied locally at each remote radio head (RRH), to reduce this fronthaul traffic. We first consider the uplink, and the case where each RRH applies a linear dimension reduction filter to its multi-antenna received signal vector. It is shown that under a joint mutual information criteria, the optimal dimension reduction filters are given by a variant of the conditional Karhunen-Loeve transform, with a stationary point found using block co-ordinate ascent. These filters are then modified such that each RRH can calculate its own dimension reduction filter in a decentralised manner, using knowledge only of its own instantaneous channel and network slow fading coefficients. We then show that in TDD systems these dimension reduction filters can be re-used as part of a two-stage reduced dimension downlink precoding scheme. Analysis and numerical results demonstrate that the proposed approach can significantly reduce both uplink and downlink fronthaul traffic whilst incurring very little loss in MIMO performance.