Channel and Power Allocation for Multi-Cell NOMA Using Multi-Agent Deep Reinforcement Learning and Unsupervised Learning

Among the 5G and anticipated 6G technologies, non-orthogonal multiple access (NOMA) has attracted considerable attention due to its notable advantages in data throughput. Nevertheless, it is challenging to find the near-optimal allocation of the channel and power resources to maximize the performanc...

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Bibliografische Detailangaben

Hauptverfasser:	Ming Sun, Yihe Zhong, Xiaoou He, Jie Zhang
Format:	Artigo
Sprache:	Inglês
Veröffentlicht:	MDPI AG 2025-04-01
Schriftenreihe:	Sensors
Schlagworte:	non-orthogonal multiple access (NOMA) channel allocation power allocation multi-agent deep reinforcement learning unsupervised learning attention mechanism
Online-Zugang:	https://www.mdpi.com/1424-8220/25/9/2733
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