Phase Optimization and Relay Selection for Joint Relay and IRS-Assisted Communication

TL;DR

This paper proposes a joint relay and IRS-assisted communication framework that combines successive refinement and reinforcement learning to optimize phase angles and relay selection, significantly improving achievable rates.

Contribution

It introduces a novel combination of successive refinement and reinforcement learning for joint phase and relay optimization in IRS-assisted systems.

Findings

Improved achievable rate performance over benchmarks

Better scalability with increasing number of relays

Effective joint optimization of phase angles and relay selection

Abstract

The use of Intelligent Reflecting Surfaces (IRSs) is considered a potential enabling technology for enhancing the spectral and energy efficiency of beyond 5G communication systems. In this paper, a joint relay and intelligent reflecting surface (IRS)-assisted communication is considered to investigate the gains of optimizing both the phase angles and selection of relays. The combination of successive refinement and reinforcement learning is proposed. Successive refinement algorithm is used for phase optimization and reinforcement learning is used for relay selection. Experimental results indicate that the proposed approach offers improved achievable rate performance and scales better with number of relays compared to considered benchmark approaches.