Partially Distributed Beamforming Design for RIS-Aided Cell-Free Networks

TL;DR

This paper introduces a partially distributed beamforming algorithm for RIS-aided cell-free networks, balancing performance and CSI exchange, and demonstrating significant improvements over traditional methods.

Contribution

It proposes a novel partially distributed beamforming design that combines local active and centralized passive beamforming for enhanced network capacity.

Findings

Achieves significant sum-rate improvement over local beamforming methods.

Demonstrates the effectiveness of RIS deployment in cell-free networks.

Reduces CSI exchange compared to fully centralized approaches.

Abstract

Cell-free networks are regarded as a promising technology to meet higher rate requirements for beyond fifth-generation (5G) communications. Most works on cell-free networks focus on either fully centralized beamforming to maximally enhance system performance, or fully distributed beamforming to avoid extensive channel state information (CSI) exchange among access points (APs). In order to achieve both network capacity improvement and CSI exchange reduction, we propose a partially distributed beamforming design algorithm for reconfigurable intelligent surface (RIS)-aided cell-free networks. We aim at maximizing the weighted sum-rate of all users by designing active and passive beamforming subject to transmit power constraints of APs and unit-modulus constraints of RIS elements. The weighted sum-rate maximization problem is first transformed into an equivalent weighted sum-mean-square-error (sum-MSE) minimization problem, and then alternating optimization (AO) approach is adopted to iteratively design active and passive beamformer. Specifically, active beamforming vectors are obtained by local APs and passive beamforming vector is optimized by central processing unit (CPU). Numerical results not only illustrate the proposed partially distributed algorithm achieves the remarkable performance improvement compared with conventional local beamforming methods, but also further show the considerable potential of deploying RIS in cell-free networks.