Intelligent Reflective Surface Resource Allocation Algorithm Based on Deep Reinforcement Learning in Internet of Vehicles System

Intelligent Reflecting Surface exhibits significant potential in 6G Vehicle-to-Everything (V2X) communications, helping to alleviate communication link blockage issues caused by the high dynamics of vehicle networks. This paper investigates the fairness resource allocation problem in IRS-assisted multi-user Multiple-Input Single-Output V2X system. By jointly optimizing the transmit beamforming at the Roadside Unit and the phase-shift matrix at the IRS to maximize the minimum vehicle users (V-UEs) rate. To tackle the non-convexity of the joint optimization, we model it as a reinforcement learning Markov decision process and propose a Deep Deterministic Policy Gradient based IRS resource allocation algorithm. The algorithm learns the optimal policy through interaction with the model. Simulation results demonstrate that the proposed algorithm enhances the minimum rate of V-UEs and ensures fairness among them.