Energy-Efficient Resource Management for Multi-UAV NOMA Networks Based on Deep Reinforcement Learning

Cellular-connected unmanned aerial vehicles (UAVs) play an essential role in cellular networks. Combined with non-orthogonal multiple access (NOMA) technique, UAVs can provide better performance in various communication scenarios. In this paper, we investigate a NOMA-enhanced UAV-assisted cellular n...

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Veröffentlicht in:	IEEE Vehicular Technology Conference S. 01 - 05
Hauptverfasser:	Lin, Xiangda, Yang, Helin, Lin, Kailong, Xiao, Liang, Shi, Zhaoyuan, Yang, Wanting, Xiong, Zehui
Format:	Tagungsbericht
Sprache:	Englisch
Veröffentlicht:	IEEE 24.06.2024
Schlagworte:	Cellular networks Clustering algorithms deep reinforcement learning Energy efficiency NOMA non-orthogonal multiple access Partitioning algorithms Quality of service resource management unmanned aerial vehicles Vehicular and wireless technologies
ISSN:	2577-2465
Online-Zugang:	Volltext
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