Multi-Scale Supervised Learning-Based Channel Estimation for RIS-Aided Communication Systems.

Motivated by the development of single image superresolution (SR) reconstruction in computer version, classic SR networks have been widely applied to the channel estimation of wireless communication system. To capture the spatial correlations in the reflection element-domain of reconfigurable intelligent surface (RIS), we propose a multi-scale supervised learning-based Laplacian pyramid wide residual network (LapWRes) to achieve the progressive reconstruction of cascaded channel in a coarse-to-fine fashion. The LapWRes can be divided vertically into feature extraction branch (FEB) and channel reconstruction branch (CRB), while it can also be viewed horizontally as multiple channel reconstruction modules (RMs) at different scales. In the FEB, the wide activation residual blocks are stacked to extract the high-frequency information of cascaded channel. In the CRB, the high-frequency and low-frequency information of cascaded channel is fused by utilizing the residual learning. Simulation results show that the LapWRes can achieve better estimation accuracy than other channel estimation schemes and faster convergence than existing SR network-based channel estimation models.