  To mitigate the effects of shadow fading and obstacle blocking,
reconfigurable intelligent surface (RIS) has become a promising technology to
improve the signal transmission quality of wireless communications by
controlling the reconfigurable passive elements with less hardware cost and
lower power consumption. However, accurate, low-latency and low-pilot-overhead
channel state information (CSI) acquisition remains a considerable challenge in
RIS-assisted systems due to the large number of RIS passive elements. In this
paper, we propose a three-stage joint channel decomposition and prediction
framework to require CSI. The proposed framework exploits the two-timescale
property that the base station (BS)-RIS channel is quasi-static and the
RIS-user equipment (UE) channel is fast time-varying. Specifically, in the
first stage, we use the full-duplex technique to estimate the channel between a
BS's specific antenna and the RIS, addressing the critical scaling ambiguity
problem in the channel decomposition. We then design a novel deep neural
network, namely, the sparse-connected long short-term memory (SCLSTM), and
propose a SCLSTM-based algorithm in the second and third stages, respectively.
The algorithm can simultaneously decompose the BS-RIS channel and RIS-UE
channel from the cascaded channel and capture the temporal relationship of the
RIS-UE channel for prediction. Simulation results show that our proposed
framework has lower pilot overhead than the traditional channel estimation
algorithms, and the proposed SCLSTM-based algorithm can also achieve more
accurate CSI acquisition robustly and effectively.
