  Reconfigurable intelligent surfaces (RISs) have been recently considered as a
promising candidate for energy-efficient solutions in future wireless networks.
Their dynamic and lowpower configuration enables coverage extension, massive
connectivity, and low-latency communications. Due to a large number of unknown
variables referring to the RIS unit elements and the transmitted signals,
channel estimation and signal recovery in RIS-based systems are the ones of the
most critical technical challenges. To address this problem, we focus on the
RIS-assisted wireless communication system and present two joint channel
estimation and signal recovery schemes based on message passing algorithms in
this paper. Specifically, the proposed bidirectional scheme applies the Taylor
series expansion and Gaussian approximation to simplify the sum-product
procedure in the formulated problem. In addition, the inner iteration that
adopts two variants of approximate message passing algorithms is incorporated
to ensure robustness and convergence. Two ambiguities removal methods are also
discussed in this paper. Our simulation results show that the proposed schemes
show the superiority over the state-of-art benchmark method. We also provide
insights on the impact of different RIS parameter settings on the proposed
schemes.
