Enhanced channel estimation for near-field IRS-aided multi-user MIMO system via a large deep residual network

Abstract

In this paper, the channel estimation (CE) of intelligent reflecting surface-aided near-field (NF) multi-user communication is investigated. Initially, the least square (LS) estimator and minimum mean square error (MMSE) estimator for the estimated channel are designed, their mean square errors (MSEs) are derived, and the Cramer-Rao lower bound (CRLB) is derived to serve as a benchmark for performance evaluation. Subsequently, in view of the fact that the NF channel model is more sensitive to distance variations compared to the far-field model, this leads to pronounced discrepancies in the user channel characteristics in different regions. To effectively capture and utilize these diverse channel features, users are initially divided into distinct regions predicated on pivotal parameters, such as channel angle and distance. Correspondingly, a user region classifier based on convolutional neural networks is designed. Then, to fully harness the potential of deep residual networks (DRNs) in denoising, the aforementioned CE problem is reconceptualized as a denoising task, and a DRN-driven single region NF CE network, named SR-DRN-NFCE, is proposed. In addition, by integrating SR-DRN-NFCE networks corresponding to different regions and conducting joint training in a federated learning (FL) manner, a new network is obtained, named FL-DRN-NFCE. Simulation results demonstrate that the proposed FL-DRN-NFCE network outperforms LS, MMSE, and no residual connections in terms of MSE, and the proposed FL-DRN-NFCE method has higher CE accuracy than the SR-DRN-NFCE method.