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This paper investigates the problem of joint massive devices separation andchannel estimation for a reconfigurable intelligent surface (RIS)-aidedunsourced random access (URA) scheme in the sixth-generation (6G) wirelessnetworks. In particular, by associating the data sequences to a rank-one tensorand exploiting the angular sparsity of the channel, the detection problem iscast as a high-order coupled tensor decomposition problem. However, thecoupling among multiple devices to RIS (device-RIS) channels together withtheir sparse structure make the problem intractable. By devising novel priorsto incorporate problem structures, we design a novel probabilistic model tocapture both the element-wise sparsity from the angular channel model and thelow rank property due to the sporadic nature of URA. Based on the thisprobabilistic model, we develop a coupled tensor-based automatic detection(CTAD) algorithm under the framework of variational inference with fastconvergence and low computational complexity. Moreover, the proposed algorithmcan automatically learn the number of active devices and thus effectively avoidnoise overfitting. Extensive simulation results confirm the effectiveness andimprovements of the proposed URA algorithm in large-scale RIS regime.

A Bayesian Tensor Approach to Enable RIS for 6G Massive Unsourced Random Access