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Reinforcement Learning-Based IRS Phase Optimization in MIMO Systems
- Citation Author(s):
- Submitted by:
- Ardavan Rahimian
- Last updated:
- Mon, 12/23/2024 - 17:04
- DOI:
- 10.21227/at91-2w29
- License:
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Abstract
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. The learning progress is visualized through a cumulative reward history plot, where rewards are computed based on achievable channel capacity.
Instructions:
To run this code, save the file as 'IRS_RL.m' and execute in MATLAB by typing: >> IRS_RL