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This paper investigates a reconfigurable intelligent surface (RIS)-aidedunsourced random access (URA) scheme for the sixth-generation (6G) wirelessnetworks with massive sporadic traffic devices. First of all, this paperproposes a novel joint active device separation (the message recovery of activedevice) and channel estimation architecture for the RIS-aided URA.Specifically, the RIS passive reflection is optimized before the successfuldevice separation. Then, by associating the data sequences to multiple rank-onetensors and exploiting the angular sparsity of the RIS-BS channel, thedetection problem is cast as a high-order coupled tensor decomposition problemwithout the need of exploiting pilot sequences. However, the inherent couplingamong multiple sparse device-RIS channels, together with the unknown number ofactive devices make the detection problem at hand deviate from the widely-usedcoupled tensor decomposition format. To overcome this challenge, this paperjudiciously devises a probabilistic model that captures both the element-wisesparsity from the angular channel model and the low-rank property due to thesporadic nature of URA. Then, based on such a probabilistic model, a iterativedetection algorithm is developed under the framework of sparse variationalinference, where each update iteration is obtained in a closed-form and thenumber of active devices can be automatically estimated for effectivelyavoiding the overfitting of noise. Extensive simulation results confirm theexcellence of the proposed URA algorithm, especially for the case of a largenumber of reflecting elements for accommodating a significantly large number ofdevices.

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