3D-Trajectory and Phase-Shift Design for RIS-Assisted UAV Systems Using Deep Reinforcement Learning

Unmanned aerial vehicle (UAV) can effectively work as temporary base station or access point in the air to transfer/receive data to/from ground terminals (GTs). However, UAV-GT links might be blocked by ground obstacles, like buildings in urban area, leading to a poor performance on data transferrin...

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Bibliographic Details
Published in:	IEEE transactions on vehicular technology Vol. 71; no. 3; pp. 3020 - 3029
Main Authors:	Mei, Haibo, Yang, Kun, Liu, Qiang, Wang, Kezhi
Format:	Journal Article
Language:	English
Published:	New York IEEE 01.03.2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	3D-trajectory Algorithms Approximation Array signal processing Autonomous aerial vehicles Base stations Decision making Deep learning deep reinforcement learning (DRL) intelligent reflecting surface (IRS) Machine learning Optimization Phase shift Propulsion Reconfigurable intelligent surface (RIS) Resource management Rotors System effectiveness unmanned aerial vehicle (UAV) Unmanned aerial vehicles Urban areas Wireless networks
ISSN:	0018-9545, 1939-9359
Online Access:	Get full text
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