Skip to main content

Statistical CSI-based Beamforming for RIS-Aided Multiuser MISO Systems via Deep Reinforcement Learning

Eskandari, Mahdi, Zhu, Huiling, Shojaeifard, Arman, Wang, Jiangzhou (2023) Statistical CSI-based Beamforming for RIS-Aided Multiuser MISO Systems via Deep Reinforcement Learning. IEEE Wireless Communications Letters, . ISSN 2162-2337. (In press) (KAR id:104067)

Abstract

This letter presents a novel joint beamforming algorithm for reconfigurable intelligent surfaces (RIS) in multiuser multiple-input single-output (MISO) wireless communications. At first, by utilizing statistical channel state information (CSI) instead of instantaneous CSI, we significantly reduce channel estimation overhead. Then, the optimization of beamforming weights is accomplished using the proximal policy optimization (PPO) algorithm, a well-established actor-critic-based reinforcement learning (RL) approach. The impact of system parameters on user sum rate is also analyzed through simulations. The results show the PPO algorithm outperforms the existing methods by combining beamforming techniques with statistical CSI.

Item Type: Article
Subjects: T Technology
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing
Funders: University of Kent (https://ror.org/00xkeyj56)
Depositing User: Mahdi Eskandari
Date Deposited: 24 Nov 2023 18:11 UTC
Last Modified: 27 Nov 2023 13:25 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/104067 (The current URI for this page, for reference purposes)

University of Kent Author Information

Eskandari, Mahdi.

Creator's ORCID: https://orcid.org/0000-0002-4334-4562
CReDIT Contributor Roles:

Zhu, Huiling.

Creator's ORCID:
CReDIT Contributor Roles:

Wang, Jiangzhou.

Creator's ORCID: https://orcid.org/0000-0003-0881-3594
CReDIT Contributor Roles:
  • Depositors only (login required):

Total unique views for this document in KAR since July 2020. For more details click on the image.