We introduce a novel deep reinforcement learning (DRL) approach to jointly optimize transmit beamforming and reconfigurable intelligent surface (RIS) phase shifts in a multiuser multiple input single output (MU-MISO) system to maximize the sum downlink rate under the phase-dependent reflection amplitude model. Our approach addresses the challenge of imperfect channel state information (CSI) and hardware impairments by considering a practical RIS amplitude model. We compare the performance of our approach against a vanilla DRL agent in two scenarios: perfect CSI and phase-dependent RIS amplitudes, and mismatched CSI and ideal RIS reflections. The results demonstrate that the proposed framework significantly outperforms the vanilla DRL agent under mismatch and approaches the golden standard. Our contributions include modifications to the DRL approach to address the joint design of transmit beamforming and phase shifts and the phase-dependent amplitude model. To the best of our knowledge, our method is the first DRL-based approach for the phase-dependent reflection amplitude model in RIS-aided MU-MISO systems. Our findings in this study highlight the potential of our approach as a promising solution to overcome hardware impairments in RIS-aided wireless communication systems.

翻译：本文提出一种新型深度强化学习（DRL）方法，用于在多用户多输入单输出（MU-MISO）系统中联合优化发射波束赋形与可重构智能表面（RIS）的相位偏移，以在相位依赖反射幅度模型下最大化下行总速率。该方法通过考虑实用的RIS幅度模型，解决了信道状态信息（CSI）不完美及硬件损伤带来的挑战。我们在两种场景下将所提方法的性能与基线DRL智能体进行比较：完美CSI与相位依赖RIS幅度场景，以及失配CSI与理想RIS反射场景。结果表明，所提框架在失配情况下性能显著优于基线DRL智能体，且接近理论最优标准。本文的贡献包括：针对发射波束赋形与相位偏移的联合设计以及相位依赖幅度模型，对DRL方法进行了改进。据我们所知，本方法是首个基于DRL的RIS辅助MU-MISO系统中相位依赖反射幅度模型的解决方案。本研究结果揭示了所提方法作为克服RIS辅助无线通信系统中硬件损伤的有前景解决方案的潜力。