Distributed Auto-Learning GNN for Multi-Cell Cluster-Free NOMA Communications

A multi-cell cluster-free NOMA framework is proposed, where both intra-cell and inter-cell interference are jointly mitigated via flexible cluster-free successive interference cancellation (SIC) and coordinated beamforming design. The joint design problem is formulated to maximize the system sum rat...

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Veröffentlicht in:	IEEE journal on selected areas in communications Jg. 41; H. 4; S. 1
Hauptverfasser:	Xu, Xiaoxia, Liu, Yuanwei, Chen, Qimei, Mu, Xidong, Ding, Zhiguo
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.04.2023 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Algorithms Array signal processing Beamforming Cluster-free SIC Clusters Computational geometry Computer architecture Convexity Data exchange graph neural network (GNN) Graph neural networks Interference Interference cancellation learning-based distributed optimization Machine learning Mixed integer NOMA non-orthogonal multiple access Nonlinear programming Optimization Processor scheduling Railway stations User requirements Wireless communication
ISSN:	0733-8716, 1558-0008
Online-Zugang:	Volltext
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