Deep Reinforcement Learning-Based Precoding for Multi-RIS-Aided Multiuser Downlink Systems with Practical Phase Shift

• URL: http://arxiv.org/abs/2509.25661v1
• Date: Tue, 30 Sep 2025 01:57:57 GMT
• Title: Deep Reinforcement Learning-Based Precoding for Multi-RIS-Aided Multiuser Downlink Systems with Practical Phase Shift
• Authors: Po-Heng Chou, Bo-Ren Zheng, Wan-Jen Huang, Walid Saad, Yu Tsao, Ronald Y. Chang,
• Abstract summary: This study considers multiple reconfigurable surfaces (RISs)-aided multiuser downlink systems with the goal of jointly optimizing the RIS phase shift matrix to spectrum efficiency.<n>The proposed DD-DRL framework is evaluated under both fixed random numbers of users in mmWave channel settings.
• Score: 44.303967859644295
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This study considers multiple reconfigurable intelligent surfaces (RISs)-aided multiuser downlink systems with the goal of jointly optimizing the transmitter precoding and RIS phase shift matrix to maximize spectrum efficiency. Unlike prior work that assumed ideal RIS reflectivity, a practical coupling effect is considered between reflecting amplitude and phase shift for the RIS elements. This makes the optimization problem non-convex. To address this challenge, we propose a deep deterministic policy gradient (DDPG)-based deep reinforcement learning (DRL) framework. The proposed model is evaluated under both fixed and random numbers of users in practical mmWave channel settings. Simulation results demonstrate that, despite its complexity, the proposed DDPG approach significantly outperforms optimization-based algorithms and double deep Q-learning, particularly in scenarios with random user distributions.

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