Energy-Efficient Trajectory Planning With Joint Device Selection and Power Splitting for mmWaves-Enabled UAV-NOMA Networks

This paper proposes two energy-efficient reinforcement learning (RL)-based algorithms for millimeter wave (mmWave)-enabled unmanned aerial vehicle (UAV) communications toward beyond-5G (B5G). This can be especially useful in ad-hoc communication scenarios within a neighborhood with main-network conn...

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Egile Nagusiak:	Ahmad Gendia, Osamu Muta, Sherief Hashima, Kohei Hatano
Formatua:	Artigo
Hizkuntza:	Inglês
Argitaratua:	IEEE 2024-01-01
Saila:	IEEE Transactions on Machine Learning in Communications and Networking
Gaiak:	NOMA resource control reinforcement learning UAV emergency communications
Sarrera elektronikoa:	https://ieeexplore.ieee.org/document/10517756/
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