U-MLP-Based Hybrid-Field Channel Estimation for XL-RIS Assisted Millimeter-Wave MIMO Systems

Abstract

In this letter, we study the hybrid-field cascaded channel estimation in the extremely large-scale RIS (XL-RIS) assisted multi-user millimeter wave systems, where the cascaded channel is composed of far-field and near-field radiation components, and has spatial non-stationarity caused by visibility regions. We propose a U-shaped network based on the dedicated multilayer perceptron (MLP) architecture, termed as U-MLP, to realize the high-dimensional channel reconstruction with limited pilot overhead. In U-MLP, a basic feature extraction module-Permutator is designed to capture the long-range dependency of non-stationary channel, while the U-shaped backbone is constructed to exploit effective latent representation of the high-dimensional cascaded channel. Numerical results show that the proposed U-MLP outperforms existing channel estimation benchmarks with less pilot overhead.