English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

We introduce a novel deep reinforcement learning (DRL) approach to jointlyoptimize transmit beamforming and reconfigurable intelligent surface (RIS)phase shifts in a multiuser multiple input single output (MU-MISO) system tomaximize the sum downlink rate under the phase-dependent reflection amplitudemodel. Our approach addresses the challenge of imperfect channel stateinformation (CSI) and hardware impairments by considering a practical RISamplitude model. We compare the performance of our approach against a vanillaDRL agent in two scenarios: perfect CSI and phase-dependent RIS amplitudes, andmismatched CSI and ideal RIS reflections. The results demonstrate that theproposed framework significantly outperforms the vanilla DRL agent undermismatch and approaches the golden standard. Our contributions includemodifications to the DRL approach to address the joint design of transmitbeamforming and phase shifts and the phase-dependent amplitude model. To thebest of our knowledge, our method is the first DRL-based approach for thephase-dependent reflection amplitude model in RIS-aided MU-MISO systems. Ourfindings in this study highlight the potential of our approach as a promisingsolution to overcome hardware impairments in RIS-aided wireless communicationsystems.

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