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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Vydané v:IEEE transactions on wireless communications Ročník 21; číslo 12; s. 10145 - 10161
Hlavní autori: Shao, Xiaodan, Cheng, Lei, Chen, Xiaoming, Huang, Chongwen, Ng, Derrick Wing Kwan
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York IEEE 01.12.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
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
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Shrnutí: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 active device) and channel estimation architecture for the RIS-aided URA. Specifically, the RIS passive reflection is optimized before the successful device separation. Then, by associating the data sequences to multiple rank-one tensors and exploiting the angular sparsity of the RIS-BS channel, the detection problem is cast as a high-order coupled tensor decomposition problem without the need of exploiting pilot sequences. However, the inherent coupling among multiple sparse device-RIS channels, together with the unknown number of active devices make the detection problem at hand deviate from the widely-used coupled tensor decomposition format. To overcome this challenge, this paper judiciously devises a probabilistic model that captures both the element-wise sparsity from the angular channel model and the low-rank property due to the sporadic nature of URA. Then, based on such a probabilistic model, a iterative detection algorithm is developed under the framework of sparse variational inference, where each update iteration is obtained in a closed-form and the number of active devices can be automatically estimated for effectively avoiding the overfitting of noise. Extensive simulation results confirm the excellence of the proposed URA algorithm, especially for the case of a large number of reflecting elements for accommodating a significantly large number of devices.
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ISSN:1536-1276
1558-2248
DOI:10.1109/TWC.2022.3182653