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：我们引入了一种新颖的深度强化学习（DRL）方法，用于联合优化多用户多输入单输出（MU-MISO）系统中的发射波束成型和可重构智能表面（RIS）相位移位，以在相依反射振幅模型下最大化总下行速率。我们的方法通过考虑实际的RIS幅度模型来解决了不完美信道状态信息（CSI）和硬件失真的挑战。我们在两个场景中将我们的方法的性能与香草DRL智能体进行了比较：完美CSI和相依RIS幅度，以及失配CSI和理想RIS反射。结果表明，在失配的情况下，所提出的框架显著优于香草DRL智能体，并接近黄金标准。我们的贡献包括修改DRL方法来解决发射波束成型和相移联合设计以及相依幅度模型。据我们所知，我们的方法是RIS辅助MU-MISO系统相依反射振幅模型的第一个DRL方法。我们在本研究中的发现突显了我们的方法作为克服RIS辅助无线通信系统硬件失真的有前途的解决方案的潜力。