Sağlam, BaturayGüngörlüoğlu, D.Kozat, Süleyman Serdar2024-03-062024-03-062023-10-23979-8-3503-3308-42694-2941https://hdl.handle.net/11693/114362Conference name: 2023 IEEE International Conference on Communications Workshops, ICC Workshops 2023Date of conference: 28 May 2023 - 01 June 2023We 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.enDeep reinforcement learningHardware impairmentMultiuser multiple input single outputPhase-dependent amplitudeReconfigurable intelligent surfaceSum rateConference Paper10.1109/ICCWorkshops57953.2023.10283517