Statistics Approximation-Enabled Distributed Beamforming for Cell-Free Massive MIMO

TL;DR

This paper introduces a distributed beamforming scheme for cell-free massive MIMO systems that uses statistical approximations to achieve near-optimal performance with reduced information exchange.

Contribution

It proposes the GSLI-MMSE scheme that leverages global statistics and local instantaneous information, enabling distributed beamforming with performance close to centralized MMSE.

Findings

GSLI-MMSE achieves performance comparable to centralized MMSE.

The scheme is effective under Rician and Rayleigh fading models.

Numerical results validate the scheme's robustness and efficiency.

Abstract

We study a distributed beamforming approach for cell-free massive multiple-input multiple-output networks, referred to as Global Statistics & Local Instantaneous information-based minimum mean-square error (GSLI-MMSE). The scenario with multi-antenna access points (APs) is considered over three different channel models: correlated Rician fading with fixed or random line-of-sight (LoS) phase-shifts, and correlated Rayleigh fading. With the aid of matrix inversion derivations, we can construct the conventional MMSE combining from the perspective of each AP, where global instantaneous information is involved. Then, for an arbitrary AP, we apply the statistics approximation methodology to approximate instantaneous terms related to other APs by channel statistics to construct the distributed combining scheme at each AP with local instantaneous information and global statistics. With the aid of uplink-downlink duality, we derive the respective GSLI-MMSE precoding schemes. Numerical results showcase that the proposed GSLI-MMSE scheme demonstrates performance comparable to the optimal centralized MMSE scheme, under the stable LoS conditions, e.g., with static users having Rician fading with a fixed LoS path.