Low-Complexity Sum-Capacity Maximization for Intelligent Reflecting Surface-Aided MIMO Systems

TL;DR

This paper introduces a low-complexity algorithm called DSM for optimizing IRS phase shifts in MIMO systems, significantly reducing computational effort while nearly maximizing sum capacity.

Contribution

The paper presents a novel sinusoidal maximization algorithm that efficiently optimizes IRS phase shifts with lower complexity compared to existing methods.

Findings

DSM achieves near-maximal sum rate.

DSM converges faster than benchmark methods.

The algorithm effectively exploits sinusoidal properties of the objective.

Abstract

Reducing computational complexity is crucial in optimizing the phase shifts of Intelligent Reflecting Surface (IRS) systems since IRS-assisted communication systems are generally deployed with a large number of reflecting elements (REs). This letter proposes a low-complexity algorithm, designated as Dimension-wise Sinusoidal Maximization (DSM), to obtain the optimal IRS phase shifts that maximize the sum capacity of a MIMO network. The algorithm exploits the fact that the objective function for the optimization problem is sinusoidal w.r.t. the phase shift of each RE. The numerical results show that DSM achieves a near-maximal sum rate and faster convergence speed than two other benchmark methods.