This MATLAB script implements a reinforcement learning (RL) approach to optimize IRS phase configurations in a MIMO wireless system. The implementation features a basic MIMO setup with a 16-element IRS operating at 12 GHz (mid-band frequency). Using the policy gradient method with a two-layer neural network, it learns optimal phase shifts while considering user mobility and Rician fading channels. The system models both direct and IRS-reflected paths, incorporating realistic path loss and channel conditions.

DOI Link: 
10.21227/at91-2w29
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[1] Ardavan Rahimian, "Reinforcement Learning-Based IRS Phase Optimization in MIMO Systems", IEEE Dataport, 2024. [Online]. Available: http://dx.doi.org/10.21227/at91-2w29. Accessed: Apr. 25, 2025.
@data{at91-2w29-24,
doi = {10.21227/at91-2w29},
url = {http://dx.doi.org/10.21227/at91-2w29},
author = {Ardavan Rahimian },
publisher = {IEEE Dataport},
title = {Reinforcement Learning-Based IRS Phase Optimization in MIMO Systems},
year = {2024} }
TY - DATA
T1 - Reinforcement Learning-Based IRS Phase Optimization in MIMO Systems
AU - Ardavan Rahimian
PY - 2024
PB - IEEE Dataport
UR - 10.21227/at91-2w29
ER -
Ardavan Rahimian. (2024). Reinforcement Learning-Based IRS Phase Optimization in MIMO Systems. IEEE Dataport. http://dx.doi.org/10.21227/at91-2w29
Ardavan Rahimian, 2024. Reinforcement Learning-Based IRS Phase Optimization in MIMO Systems. Available at: http://dx.doi.org/10.21227/at91-2w29.
Ardavan Rahimian. (2024). "Reinforcement Learning-Based IRS Phase Optimization in MIMO Systems." Web.
1. Ardavan Rahimian. Reinforcement Learning-Based IRS Phase Optimization in MIMO Systems [Internet]. IEEE Dataport; 2024. Available from : http://dx.doi.org/10.21227/at91-2w29
Ardavan Rahimian. "Reinforcement Learning-Based IRS Phase Optimization in MIMO Systems." doi: 10.21227/at91-2w29