  As an emerging communication auxiliary technology, reconfigurable intelligent
surface (RIS) is expected to play a significant role in the upcoming 6G
networks. Due to its total reflection characteristics, it is challenging to
implement conventional channel estimation algorithms. This work focuses on
RIS-assisted MIMO communications. Although many algorithms have been proposed
to address this issue, there are still ample opportunities for improvement in
terms of estimation accuracy, complexity, and applicability. To fully exploit
the structured sparsity of the multiple-input-multiple-output (MIMO) channels,
we propose a new channel estimation algorithm called unitary approximate
message passing sparse Bayesian learning with partial common support
identification (UAMPSBL-PCI). Thanks to the mechanism of PCI and the use of
UAMP, the proposed algorithm has a lower complexity while delivering enhanced
performance relative to existing channel estimation algorithms. Extensive
simulations demonstrate its excellent performance in various environments.
