Efficient Joint Precoding Design for Wideband Intelligent Reflecting Surface-Assisted Cell-Free Network

TL;DR

This paper introduces an efficient joint precoding method for wideband IRS-assisted cell-free networks, optimizing active and passive beamforming to significantly improve weighted sum-rate performance.

Contribution

It presents a novel fractional programming-based approach with alternating optimization for joint precoding in wideband IRS-assisted networks, addressing frequency selectivity and high-dimensional non-convex challenges.

Findings

Achieves significant weighted sum-rate improvements over baseline methods.

Effectively handles frequency selectivity of IRS in wideband transmissions.

Provides insights into reducing computational complexity and enhancing network capacity.

Abstract

In this paper, we propose an efficient joint precoding design method to maximize the weighted sum-rate in wideband intelligent reflecting surface (IRS)-assisted cell-free networks by jointly optimizing the active beamforming of base stations and the passive beamforming of IRS. Due to employing wideband transmissions, the frequency selectivity of IRSs has to been taken into account, whose response usually follows a Lorentzian-like profile. To address the high-dimensional non-convex optimization problem, we employ a fractional programming approach to decouple the non-convex problem into subproblems for alternating optimization between active and passive beamforming. The active beamforming subproblem is addressed using the consensus alternating direction method of multipliers (CADMM) algorithm, while the passive beamforming subproblem is tackled using the accelerated projection gradient (APG) method and Flecher-Reeves conjugate gradient method (FRCG). Simulation results demonstrate that our proposed approach achieves significant improvements in weighted sum-rate under various performance metrics compared to primal-dual subgradient (PDS) with ideal reflection matrix. This study provides valuable insights for computational complexity reduction and network capacity enhancement.