Channel estimation study of reconfigurable intelligent surface aided system based on WGAN

Aiming at the problem that the system is complex and it is difficult to obtain accurate channel state information (CSI) in millimeter wave communication assisted by reconfigurable intelligent surface (RIS), this paper designs a channel estimation scheme of Chan-SRWGAN network algorithm. The scheme adopts a hybrid active / passive RIS architecture. Firstly, the least square (LS) algorithm is used to obtain the channel estimation value at the active elements, and then the preliminary channel estimation is obtained by interpolation. Finally, the Chan-SRWGAN deep learning network is used to reconstruct it into accurate channel estimation. Simulation results show that the proposed scheme outperforms LS, orthogonal matching pursuit (OMP), simultaneous orthogonal matching pursuit (SOMP), deep neural network (DNN), super-resolution convolutional neural network (SRCNN) channel estimation algorithms in terms of normalized mean squared error (NMSE) performance, thus confirming the feasibility of the approach.