Deep Learning for Super-Resolution Channel Estimation in Reconfigurable Intelligent Surface Aided Systems

Abstract

Reconfigurable intelligent surface (RIS) enables the configuration of the propagation environment. Channel estimation is an essential task in realizing the RIS-aided communication system. A RIS-aided multi-user multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) communication system involves cascaded channels with high dimensions and sophisticated statistics. Thus, implementing the optimal minimum mean square error (MMSE) with the integration computation is infeasible in practice. To accurately estimate channels with high accuracy in a RIS-aided multi-user MIMO-OFDM system, we model the channel state information (CSI) estimation as an image super-resolution (SR) problem to recover and denoise the channel matrix. Particularly, a convolutional neural network based on a super-resolution convolutional neural network (SRCNN) and denoising convolutional neural network (DnCNN), named SRDnNet, is then proposed. By taking estimated channels at pilot positions as a low-resolution image, the enhanced SRCNN can fully exploit the features of inputs to learn a suitable interpolation method and generate the coarse estimation of the channel matrix. The denoising model DnCNN with an element-wise subtraction structure can exploit features of the additive noise and recover channel coefficients from the coarse channel matrix. The simulation results demonstrate the effectiveness and excellent performance of the proposed SRDnNet.