Reinforcement Learning in Multiple-UAV Networks: Deployment and Movement Design

A novel framework is proposed for quality of experience driven deployment and dynamic movement of multiple unmanned aerial vehicles (UAVs). The problem of joint non-convex three-dimensional (3-D) deployment and dynamic movement of the UAVs is formulated for maximizing the sum mean opinion score of g...

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Veröffentlicht in:	IEEE transactions on vehicular technology Jg. 68; H. 8; S. 8036 - 8049
Hauptverfasser:	Liu, Xiao, Liu, Yuanwei, Chen, Yue
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.08.2019 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Base stations Convergence Dynamic movement Genetic algorithms Heuristic algorithms Iterative methods Machine learning Optimization Partitioning algorithms Q-learning quality of experience (QoE) Reinforcement learning Three dimensional motion three-dimensional deployment Three-dimensional displays Trajectory unmanned aerial vehicle (UAV) Unmanned aerial vehicles Vehicle dynamics
ISSN:	0018-9545, 1939-9359
Online-Zugang:	Volltext
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