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Title: RIS-Assisted ABS for Mobile Multi-User MISO Wireless Communications: A Deep Reinforcement Learning Approach
In response to the evolving landscape of wireless communication networks and the escalating demand for unprecedented wireless connectivity performance in the forthcoming 6G era, this paper proposes a new 6G architecture to enhance the wireless network's sum rate performance. Therefore, we introduce an aerial base station (ABS) network with reconfigurable intelligent surfaces (RISs) while leveraging the multi-users multiple-input single-output (MU-MISO) antenna technology. The motivation behind our proposal stems from the imperative to address critical challenges in contemporary wireless networks and harness emerging technologies for substantial performance gains. We employ deep reinforcement learning (DRL) to jointly optimize the ABS trajectories, the active beamforming weights, and the RIS phase shifts. Simulation results show that this joint optimization effectively improves the system's sum rate while meeting minimum quality of service (QoS) requirements for diverse mobile users.  more » « less
Award ID(s):
2030291
PAR ID:
10566850
Author(s) / Creator(s):
; ;
Publisher / Repository:
IEEE
Date Published:
ISSN:
1938-1883
ISBN:
978-1-7281-9054-9
Page Range / eLocation ID:
648 to 653
Subject(s) / Keyword(s):
6G wireless deep reinforcement learning eavesdropping RIS sum rate QoS UAV ABS DDPG
Format(s):
Medium: X
Location:
Denver, CO, USA
Sponsoring Org:
National Science Foundation
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