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

B Saglam, D Gurgunoglu… - 2023 IEEE International …, 2023 - ieeexplore.ieee.org
We introduce a novel deep reinforcement learning (DRL) approach to jointly optimize
transmit beamforming and reconfigurable intelligent surface (RIS) phase shifts in a multiuser …
被引用次数：8 相关文章

Deep Reinforcement Learning Based JointDownlink Beamforming and RIS Configuration in RIS-aided MU-MISO Systems Under HardwareImpairments and Imperfect …

B Saglam, D Gurgunoglu, SS Kozat - 2022 - diva-portal.org
We investigate the joint transmit beamforming and reconfigurable intelligent surface (RIS)
configuration problem to maximize the sum downlink rate of a RIS-aided cellular multiuser …

Deep Reinforcement Learning Based Joint Downlink Beamforming and RIS Configuration in RIS-aided MU-MISO Systems Under Hardware Impairments and …

B Saglam, D Gurgunoglu, SS Kozat - arXiv e-prints, 2022 - ui.adsabs.harvard.edu
We introduce a novel deep reinforcement learning (DRL) approach to jointly optimize
transmit beamforming and reconfigurable intelligent surface (RIS) phase shifts in a multiuser …

Deep Reinforcement Learning Based Joint Downlink Beamforming and RIS Configuration in RIS-aided MU-MISO Systems Under Hardware Impairments and …

B Saglam, D Gurgunoglu, SS Kozat - arXiv preprint arXiv:2211.09702, 2022 - arxiv.org
We introduce a novel deep reinforcement learning (DRL) approach to jointly optimize
transmit beamforming and reconfigurable intelligent surface (RIS) phase shifts in a multiuser …