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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Vydané v:	IEEE transactions on vehicular technology Ročník 71; číslo 3; s. 3020 - 3029
Hlavní autori:	Mei, Haibo, Yang, Kun, Liu, Qiang, Wang, Kezhi
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York IEEE 01.03.2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	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
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