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

TL;DR

This work studies an IRS-assisted lossy communication system under correlated Rayleigh fading, deriving outage probability considerations and linking the distortion constraint to a channel threshold via Shannon separation. It then develops a two-stage deep reinforcement learning framework (DDPG-based) to optimize IRS phase shifts and maximize the received power, validated by Monte-Carlo simulations. Key findings are that channel correlation raises outage probability and that the DRL-based optimization approaches the theoretical limit, with the gap growing at higher transmit power and distortion. The work provides a practical methodology for reliable IRS-enabled communications in correlated environments and highlights the impact of correlation on system reliability.

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.

Figures (6)

• Figure 1: IRS-assisted outdoor communication system model.
• Figure 2: Correlated Rayleigh fading channel model.
• Figure 3: Performance of the proposed algorithm for diverse episodes.
• Figure 4: Reward with episodes in a sample
• Figure 5: Theoretical outage probability with $D=0$.