论文标题
在硬件障碍和不完美的CSI下，基于RIS AD的MU-MISO系统中的基于深度学习的联合下行链接和RIS配置
Deep Reinforcement Learning Based Joint Downlink Beamforming and RIS Configuration in RIS-aided MU-MISO Systems Under Hardware Impairments and Imperfect CSI
论文作者
论文摘要
我们介绍了一种新型的深入增强学习方法（DRL）方法，以共同优化多源多多输入单输出（MU-MISO）系统中的发射光束形成和可重新配置的智能表面（RIS）相移，以最大程度地利用相位依赖性的反射振幅模型的总和下行链接速率。我们的方法通过考虑实用的RIS振幅模型来应对不完美的渠道状态信息（CSI）和硬件障碍的挑战。在两种情况下，我们将方法的性能与香草DRL代理的性能进行了比较：完美的CSI和相关的RIS振幅，以及不匹配的CSI和理想的RIS反射。结果表明，所提出的框架在不匹配下明显优于香草DRL代理，并接近黄金标准。我们的贡献包括对DRL方法的修改，以解决发射光束形成和相移的关节设计以及相关振幅模型。据我们所知，我们的方法是RIS AID的MU-MISO系统中基于相关反射幅度模型的第一种基于DRL的方法。我们在这项研究中的发现突出了我们方法作为克服RIS辅助无线通信系统中硬件障碍的有前途解决方案的潜力。
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.
