IRS-Assisted Lossy Communications Under Correlated Rayleigh Fading: Outage Probability Analysis and Optimization

TL;DR

This paper analyzes the outage probability of IRS-assisted lossy communication under correlated Rayleigh fading and proposes a DRL-based method to optimize IRS phase shifts, demonstrating its effectiveness through simulations.

Contribution

It introduces a correlated channel model for IRS-assisted systems and develops a DRL-based optimization approach for phase shifts, enhancing system performance.

Findings

Outage probability increases with channel correlation.

DRL method outperforms baseline approaches.

Performance gap widens with higher transmit power.

Abstract

This paper focuses on an intelligent reflecting surface (IRS)-assisted lossy communication system with correlated Rayleigh fading. We analyze the correlated channel model and derive the outage probability of the system. Then, we design a deep reinforce learning (DRL) method to optimize the phase shift of IRS, in order to maximize the received signal power. Moreover, this paper presents results of the simulations conducted to evaluate the performance of the DRL-based method. The simulation results indicate that the outage probability of the considered system increases significantly with more correlated channel coefficients. Moreover, the performance gap between DRL and theoretical limit increases with higher transmit power and/or larger distortion requirement.