A Low-Complexity Algorithmic Framework for Large-Scale IRS-Assisted Wireless Systems

TL;DR

This paper introduces a low-complexity, convergent algorithmic framework for optimizing large-scale IRS-assisted wireless systems, significantly improving computational efficiency while maintaining high performance.

Contribution

It proposes a novel algorithmic framework combining alternating optimization and gradient methods tailored for large-scale IRS systems, with proven convergence.

Findings

Significant speedup over existing algorithms

Achieves comparable or better system performance

Effective optimization of large-scale IRS configurations

Abstract

Intelligent reflecting surfaces (IRSs) are revolutionary enablers for next-generation wireless communication networks, with the ability to customize the radio propagation environment. To fully exploit the potential of IRS-assisted wireless systems, reflective elements have to be jointly optimized with conventional communication techniques. However, the resulting optimization problems pose significant algorithmic challenges, mainly due to the large-scale non-convex constraints induced by the passive hardware implementations. In this paper, we propose a low-complexity algorithmic framework incorporating alternating optimization and gradient-based methods for large-scale IRS-assisted wireless systems. The proposed algorithm provably converges to a stationary point of the optimization problem. Extensive simulation results demonstrate that the proposed framework provides significant speedups compared with existing algorithms, while achieving a comparable or better performance.