Joint Active and Passive Beamforming for Intelligent Reflecting Surface Aided Multiuser MIMO Communications

TL;DR

This paper proposes novel joint active and passive beamforming algorithms for IRS-assisted multiuser MIMO systems, significantly improving sum-rate performance through efficient optimization techniques.

Contribution

It introduces a matrix weighted mean square error minimization framework and develops low complexity algorithms for IRS passive beamforming optimization.

Findings

Algorithms converge reliably.

Significant performance gains over non-IRS systems.

Effective optimization methods for joint beamforming.

Abstract

This letter investigates the joint active and passive beamforming optimization for intelligent reflecting surface (IRS) aided multiuser multiple-input multiple-output systems with the objective of maximizing the weighted sum-rate. We show that this problem can be solved via a matrix weighted mean square error minimization equivalence. In particular, for the optimization of the passive IRS beamforming, we first propose an iterative algorithm with excellent performance based on the manifold optimization. By using the matrix fractional programming technique to obtain a more tractable object function, we then propose a low complexity algorithm based on the majorization-minimization method. Numerical results verify the convergence of our proposed algorithms and the significant performance improvement over the communication scenario without IRS assistance.