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...

Full description

Saved in:

Bibliographic Details
Published in:	IEEE journal on selected areas in communications Vol. 41; no. 4; p. 1
Main Authors:	Xu, Xiaoxia, Liu, Yuanwei, Chen, Qimei, Mu, Xidong, Ding, Zhiguo
Format:	Journal Article
Language:	English
Published:	New York IEEE 01.04.2023 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	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 Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Be the first to leave a comment!