Enhanced Channel Estimation Performance-Based Intelligent Reflecting Surface Massive MIMO Systems

Abstract

Intelligent reflecting surfaces (IRS) are an innovative technique that dramatically increases system efficiency. The integration of massive multiple-input multiple-output (massive MIMO) and IRS has been considered the most efficient route to 6G networks. An important challenge in IRS-aided massive MIMO wireless systems is channel estimation. With a rise in the number of IRS-reflecting elements and IRS-assisted users, channel training overhead becomes too large, resulting in large transmission delays and poor data transfer rates. To overcome this problem, an enhanced compressive sensing (CS) method to determine reliable channel state information (CSI) in IRS-aided massive MIMO systems is proposed, which combines enhanced compressive sensing with a deep denoising convolution neural network (CsiNet-DeCNN). By using deep learning methods to denoise channel data, our proposed model is validated numerically, indicating that it is accurate with low NMSE. Further, the results indicate that CsiNet-DeCNN performs better than traditional CS methods in estimating channel parameters.