RIS-Aided Cell-Free Massive MIMO: Performance Analysis and Competitiveness

TL;DR

This paper analyzes a cell-free massive MIMO system enhanced with reconfigurable intelligent surfaces, deriving performance metrics and demonstrating significant coverage and throughput improvements with RIS integration.

Contribution

It introduces a novel RIS-assisted CF-mMIMO model with random beamforming, deriving a closed-form achievable rate expression and evaluating system performance under imperfect CSI.

Findings

RIS improves coverage and system performance.

Nearly 2-fold increase in minimum rate with RIS.

RIS can replace dense AP deployment with fewer RISs.

Abstract

In this paper, we consider and study a cell-free massive MIMO (CF-mMIMO) system aided with reconfigurable intelligent surfaces (RISs), where a large number of access points (APs) cooperate to serve a smaller number of users with the help of RIS technology. We consider imperfect channel state information (CSI), where each AP uses the local channel estimates obtained from the uplink pilots and applies conjugate beamforming for downlink data transmission. Additionally, we consider random beamforming at the RIS during both training and data transmission phases. This allows us to eliminate the need of estimating each RIS assisted link, which has been proven to be a challenging task in literature. We then derive a closed-form expression for the achievable rate and use it to evaluate the system's performance supported with numerical results. We show that the RIS provided array gain improves the system's coverage, and provides nearly a 2-fold increase in the minimum rate and a 1.5-fold increase in the per-user throughput. We also use the results to provide preliminary insights on the number of RISs that need to be used to replace an AP, while achieving similar performance as a typical CF-mMIMO system with dense AP deployment.