Asymptotic Analysis of the Downlink in Cooperative Massive MIMO Systems

TL;DR

This paper derives asymptotic performance expressions for cooperative massive MIMO downlink systems, revealing system trade-offs, optimizing power allocation locally, and handling complex dependencies with novel bounds.

Contribution

It provides the first closed-form asymptotic analysis of cooperative massive MIMO downlink, including a power allocation algorithm that reduces coordination overhead.

Findings

Asymptotic expressions accurately predict performance with moderate antennas

Power allocation can be optimized locally at each base station

Novel bounds on dependent shot-noise point processes are introduced

Abstract

We consider the downlink of a cooperative cellular communications system, where several base-stations around each mobile cooperate and perform zero-forcing to reduce the received interference at the mobile. We derive closed-form expressions for the asymptotic performance of the network as the number of antennas per base station grows large. These expressions capture the trade off between various system parameters, and characterize the joint effect of noise and interference (where either noise or interference is asymptotically dominant and where both are asymptotically relevant). The asymptotic results are verified using Monte Carlo simulations, which indicate that they are useful even when the number of antennas per base station is only moderately large. Additionally, we show that when the number of antennas per base station grows large, power allocation can be optimized locally at each base station. We hence present a power allocation algorithm that achieves near optimal performance while significantly reducing the coordination overhead between base stations. The presented analysis is significantly more challenging than the uplink analysis, due to the dependence between beamforming vectors of nearby base stations. This statistical dependence is handled by introducing novel bounds on marked shot-noise point processes with dependent marks, which are also useful in other contexts.