Semi-Blind Cascaded Channel Estimation for Reconfigurable Intelligent Surface Aided Massive MIMO

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 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 large number of passive RIS elements. The state-of-the-art approach for CSI acquisition in such a system is a pure training-based strategy that hinges 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 jointly estimates the channels and the partially unknown transmit signals with a small number of pilot sequences. Specifically, we formulate the semi-blind channel estimation as a trilinear matrix factorization task. Under the Bayesian inference framework, we develop a computationally efficient iterative algorithm using the sum-product 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 algorithm in the large-system limit. Extensive simulation results demonstrate the effectiveness of the proposed semi-blind channel estimation algorithm.