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

arXiv: https://arxiv.org/abs/2408.06969 · HTML: https://mlsanity.com/paper/2408.06969/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 14 references that are in the map.
- First posted on arXiv 13 Aug 2024.

## Builds on (most cited first, 4 of 4)

- [Intelligent Reflecting Surface Enhanced Wireless Network via Joint Active and Passive Beamforming](https://mlsanity.com/paper/1810.03961/) (2018)
- [Energy-Efficient Design of IRS-NOMA Networks](https://mlsanity.com/paper/2009.05344/) (2020)
- [A Survey of Blockchain and Artificial Intelligence for 6G Wireless Communications](https://mlsanity.com/paper/2305.08604/) (2023)
- [Reconfigurable Intelligent Surface Equipped UAV in Emergency Wireless Communications: A New Fading-Shadowing Model and Performance Analysis](https://mlsanity.com/paper/2406.11241/) (2024)

## Abstract (arXiv)

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.