In this paper, we address the channel estimation (CE) problem in SIM-based multi-user (MU) millimeter-wave (mmWave) near-field communication systems. To address the severe path loss and blockage in mmWave communication systems, many meta-atoms are typically integrated into each layer of the SIM. Then, the number of radio frequency (RF) chains at the base station (BS) is fewer than that of meta-atoms per layer, resulting in an underdetermined problem. Additionally, the increase in the number of meta-atoms in each layer expands the SIM's near-field region, leading to the user equipment (UEs) being mostly situated in this region, necessitating precise modeling of the channel under the spherical wavefront assumption. To address these issues, we introduce a compressed sensing (CS)-based CE protocol to tackle the underdetermined problem. In contrast to the traditional CS-based estimation framework, we investigate a polar-domain channel representation to tackle the severe energy spread effect of the classical angular-domain channel representation in near-field communication systems. Specifically, we design a novel polar-domain transform matrix for uniform planar arrays (UPAs), thereby transforming the CE problem into a sparse recovery task of the paths' support set and complex gains. To overcome the limitations of the sparse Bayesian learning (SBL) framework in tackling high-dimensional dictionaries, we propose a low-complexity polar-domain SBL (LCPD-SBL) algorithm, which significantly reduces computational complexity without compromising estimation accuracy.

翻译：本文研究了基于可重构智能表面（SIM）的多用户毫米波近场通信系统中的信道估计（CE）问题。为应对毫米波通信系统的严重路径损耗和阻塞效应，SIM每层通常集成大量超原子。这使得基站（BS）的射频（RF）链路数少于每层超原子数，从而形成欠定问题。此外，每层超原子数量的增加扩大了SIM的近场区域，导致用户设备（UE）主要分布在该区域，需基于球面波假设精确建模信道。针对这些问题，我们提出一种基于压缩感知（CS）的信道估计协议以解决欠定问题。不同于传统CS估计框架，我们研究极化域信道表示方法，以应对近场通信系统中经典角度域信道表示造成的严重能量扩散效应。具体而言，针对均匀平面阵列（UPA）设计了一种新型极化域变换矩阵，从而将信道估计问题转化为路径支撑集与复增益的稀疏恢复任务。为克服稀疏贝叶斯学习（SBL）框架处理高维字典时的局限性，我们提出低复杂度极化域SBL（LCPD-SBL）算法，该算法在保证估计精度的前提下显著降低了计算复杂度。