Reconfigurable Intelligent Surface-Aided 6G Massive Access: Coupled Tensor Modeling and Sparse Bayesian Learning

This paper investigates a reconfigurable intelligent surface (RIS)-aided unsourced random access (URA) scheme for the sixth-generation (6G) wireless networks with massive sporadic traffic devices. First of all, this paper proposes a novel joint active device separation (the message recovery of activ...

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Veröffentlicht in:	IEEE transactions on wireless communications Jg. 21; H. 12; S. 10145 - 10161
Hauptverfasser:	Shao, Xiaodan, Cheng, Lei, Chen, Xiaoming, Huang, Chongwen, Ng, Derrick Wing Kwan
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.12.2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	6G mobile communication Algorithms Bayes methods Channel estimation Decomposition higher-order coupled tensor Iterative methods Machine learning Massive unsourced random access Mathematical analysis Performance evaluation Probabilistic logic Probabilistic models Random access reconfigurable intelligent surface Reconfigurable intelligent surfaces Separation Sequences sparse Bayesian learning Sparsity Tensors Wireless networks
ISSN:	1536-1276, 1558-2248
Online-Zugang:	Volltext
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