Deep reinforcement learning based joint downlink beamforming and RIS configuration in RIS-aided MU-MISO systems under hardware impairments and imperfect CSI

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buir.contributor.authorSağlam, Baturay
buir.contributor.authorKozat, Süleyman Serdar
buir.contributor.orcidSağlam, Baturay|0000-0002-8324-5980
buir.contributor.orcidKozat, Süleyman Serdar|0000-0002-6488-3848
dc.citation.epage72en_US
dc.citation.spage66
dc.contributor.authorSağlam, Baturay
dc.contributor.authorGüngörlüoğlu, D.
dc.contributor.authorKozat, Süleyman Serdar
dc.coverage.spatialRome, Italy
dc.date.accessioned2024-03-06T13:24:51Z
dc.date.available2024-03-06T13:24:51Z
dc.date.issued2023-10-23
dc.departmentDepartment of Electrical and Electronics Engineering
dc.descriptionConference name: 2023 IEEE International Conference on Communications Workshops, ICC Workshops 2023
dc.descriptionDate of conference: 28 May 2023 - 01 June 2023
dc.description.abstractWe 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.
dc.description.provenanceMade available in DSpace on 2024-03-06T13:24:51Z (GMT). No. of bitstreams: 1 Deep_reinforcement_learning_based_joint_downlink_beamforming_and_RIS_configuration_in_RIS-aided_MU-MISO_systems_under_hardware_impairments_and_imperfect_CSI.pdf: 4438617 bytes, checksum: 597c35be114ce34921c0dabcc3801534 (MD5) Previous issue date: 2023-10-23en
dc.identifier.doi10.1109/ICCWorkshops57953.2023.10283517
dc.identifier.isbn979-8-3503-3308-4
dc.identifier.issn2694-2941
dc.identifier.urihttps://hdl.handle.net/11693/114362
dc.language.isoen
dc.publisherIEEE
dc.relation.isversionofhttps://dx.doi.org/10.1109/ICCWorkshops57953.2023.10283517
dc.source.title2023 IEEE International Conference on Communications Workshops (ICC Workshops)
dc.subjectDeep reinforcement learning
dc.subjectHardware impairment
dc.subjectMultiuser multiple input single output
dc.subjectPhase-dependent amplitude
dc.subjectReconfigurable intelligent surface
dc.subjectSum rate
dc.titleDeep reinforcement learning based joint downlink beamforming and RIS configuration in RIS-aided MU-MISO systems under hardware impairments and imperfect CSI
dc.typeConference Paper

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