IRS-Assisted Lossy Communications Under Correlated Rayleigh Fading: Outage Probability Analysis and Optimization
This work addresses performance optimization for wireless communication systems using IRS, but it is incremental as it builds on existing IRS and DRL methods with specific channel correlation considerations.
The paper tackled the problem of optimizing outage probability in IRS-assisted lossy communications under correlated Rayleigh fading by deriving analytical expressions and designing a DRL method for phase shift optimization, with simulation results showing that outage probability increases significantly with more correlated channel coefficients and performance gaps widen with higher transmit power or larger distortion requirements.
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.