用强化学习优化智能反射面，提升毫米波多用户系统频谱效率
Deep Reinforcement Learning-Based Precoding for Multi-RIS-Aided Multiuser Downlink Systems with Practical Phase Shift
- 采用深度确定性策略梯度算法联合优化发射端预编码与智能反射面相位
- 在随机用户分布下频谱效率提升超传统方法，尤其在毫米波信道中表现优异
- 首次考虑实际反射面幅度与相位耦合效应，适合无线系统设计与优化研究者
本研究针对多智能反射面（RIS）辅助的多用户下行链路系统，旨在联合优化发射端预编码与RIS相位矩阵以最大化频谱效率。不同于以往假设理想反射率的研究，本文考虑了RIS单元反射幅度与相位移之间的实际耦合效应，导致优化问题非凸。为此，提出基于深度确定性策略梯度（DDPG）的深度强化学习框架。该模型在固定和随机用户数的毫米波信道环境下进行评估。仿真结果表明，尽管复杂度较高，所提DDPG方法显著优于基于优化的算法及双深度Q学习，在随机用户分布场景下表现尤为突出。
原文摘要 · Abstract (English)
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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