  This paper investigates the problem of joint massive devices separation and
channel estimation for a reconfigurable intelligent surface (RIS)-aided
unsourced random access (URA) scheme in the sixth-generation (6G) wireless
networks. In particular, by associating the data sequences to a rank-one tensor
and exploiting the angular sparsity of the channel, the detection problem is
cast as a high-order coupled tensor decomposition problem. However, the
coupling among multiple devices to RIS (device-RIS) channels together with
their sparse structure make the problem intractable. By devising novel priors
to incorporate problem structures, we design a novel probabilistic model to
capture both the element-wise sparsity from the angular channel model and the
low rank property due to the sporadic nature of URA. Based on the this
probabilistic model, we develop a coupled tensor-based automatic detection
(CTAD) algorithm under the framework of variational inference with fast
convergence and low computational complexity. Moreover, the proposed algorithm
can automatically learn the number of active devices and thus effectively avoid
noise overfitting. Extensive simulation results confirm the effectiveness and
improvements of the proposed URA algorithm in large-scale RIS regime.
