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

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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Bibliographic Details
Main Authors: Ming Sun, Yihe Zhong, Xiaoou He, Jie Zhang
Format: Article
Language:English
Published: MDPI AG 2025-04-01
Series:Sensors
Subjects:
non-orthogonal multiple access (NOMA)
channel allocation
power allocation
multi-agent deep reinforcement learning
unsupervised learning
attention mechanism
Online Access:https://www.mdpi.com/1424-8220/25/9/2733
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