Paper Info
Title
Simultaneously Transmitting and Reflecting Reconfigurable Intelligent Surface (STAR-RIS) Assisted UAV Communications
Abstract
A novel air-to-ground communication paradigm is conceived, where an unmanned aerial vehicle (UAV)-mounted base station (BS) equipped with multiple antennas sends information to multiple ground users (GUs) with the aid of a simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS). In contrast to the conventional RIS whose main function is to reflect incident signals, the STAR-RIS is capable of both transmitting and reflecting the impinging signals from either side of the surface, thereby leading to full-space 360 degree coverage. However, the transmissive and reflective capabilities of the STAR-RIS require more complex transmission/reflection coefficient design. Therefore, in this work, a sum-rate maximization problem is formulated for the joint optimization of the UAV’s trajectory, the active beamforming at the UAV, and the passive transmission/reflection beamforming at the STAR-RIS. This cutting-edge optimization problem is also subject to the UAV’s flight safety, to the maximum flight duration constraint, as well as to the GUs’ minimum data rate requirements. Given the unknown locations of obstacles prior to the UAV’s flight, we provide an online decision making framework employing reinforcement learning (RL) to simultaneously adjust both the UAV’s trajectory as well as the active and passive beamformer. To enhance the system’s robustness against the associated uncertainties caused by limited sampling of the environment, a novel “distributionally-robust” RL (DRRL) algorithm is proposed for offering an adequate worst-case performance guarantee. Our numerical results unveil that: 1) the STAR-RIS assisted UAV communications benefit from significant sum-rate gain over the conventional reflecting-only RIS; and 2) the proposed DRRL algorithm achieves both more stable and more robust performance than the state-of-the-art RL algorithms.
Year
DOI
Venue
2022
10.1109/JSAC.2022.3196102
IEEE Journal on Selected Areas in Communications
Keywords
DocType
Volume
Air-to-ground communications,simultaneously transmitting and reflecting reconfigurable intelligent surface,joint beamforming design,collision avoidance,distributionally-robust reinforcement learning
Journal
40
Issue
ISSN
Citations 
10
0733-8716
0
PageRank 
References 
Authors
0.34
34
6
Name
Order
Citations
PageRank
Jin Zhao103.72
Yanmin Zhu21767142.50
mu xu3510.29
Kai-Quan Cai45410.16
Yuanwei Liu52162131.65
Lajos Hanzo610889849.85