Multi-Agent Reinforcement Learning Based 3D Trajectory Design in Aerial-Terrestrial Wireless Caching Networks

This paper investigates a dynamic 3D trajectory design of multiple cache-enabled unmanned aerial vehicles (UAVs) in a wireless device-to-device (D2D) caching network with the goal of maximizing the long-term network throughput. By storing popular content at the nearby mobile user devices, D2D cachin...

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Veröffentlicht in:	IEEE transactions on vehicular technology Jg. 70; H. 8; S. 8201 - 8215
Hauptverfasser:	Chen, Yu-Jia, Liao, Kai-Min, Ku, Meng-Lin, Tso, Fung Po, Chen, Guan-Yi
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.08.2021 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Algorithms Cache storage Caching Device-to-device communication Electronic devices Machine learning multi-agent reinforcement learning Multiagent systems Network topologies Nodes Storage capacity Throughput Trajectory trajectory design Trajectory optimization Unmanned aerial vehicles Unmanned aerial vehicles (UAVs) wireless caching Wireless communication Wireless networks Wireless sensor networks
ISSN:	0018-9545, 1939-9359
Online-Zugang:	Volltext
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