  Reconfigurable intelligent surface (RIS) is envisioned to be a promising
green technology to reduce the energy consumption and improve the coverage and
spectral efficiency of massive multiple-input multiple-output (MIMO) wireless
networks. In a RIS-aided MIMO system, the acquisition of channel state
information (CSI) is important for achieving passive beamforming gains of the
RIS, but is also challenging due to the cascaded property of the
transmitter-RIS-receiver channel and the lack of signal processing capability
of the passive RIS elements. The state-of-the-art approach for CSI acquisition
in such a system is a pure training-based strategy that depends on a long
sequence of pilot symbols. In this paper, we investigate semi-blind cascaded
channel estimation for RIS-aided massive MIMO systems, in which the receiver
simultaneously estimates the channel coefficients and the partially unknown
transmit signal with a small number of pilot sequences. Specifically, we
formulate the semi-blind cascaded channel estimation as a trilinear matrix
factorization task. Under the Bayesian inference framework, we develop a
computationally efficient iterative algorithm using the approximate message
passing principle to resolve the trilinear inference problem. Meanwhile, we
present an analytical framework to characterize the theoretical performance
bound of the proposed approach in the large-system limit via the replica method
developed in statistical physics. Extensive simulation results demonstrate the
effectiveness of the proposed semi-blind cascaded channel estimation algorithm.
