Sparse Channel Estimation for SIM-based mmWave Near-Field Communications

2026-06-14

AI Summary

This work addresses the underdetermined channel estimation problem in millimeter-wave near-field multiuser communications with intelligent reflecting surfaces (IRSs), which arises from insufficient radio-frequency chains and the energy dispersion caused by spherical wavefronts in the conventional angular domain. To tackle these challenges, the authors propose a compressive sensing–based channel estimation method that constructs a polar-domain transformation matrix tailored for uniform planar arrays. The channel is formulated as a sparse recovery problem involving path support sets and complex gains, and a low-complexity polar-domain sparse Bayesian learning (SBL) algorithm is developed for efficient solution. By innovatively adopting a polar-domain representation, the method effectively mitigates near-field energy dispersion, significantly reduces the computational burden of high-dimensional dictionary construction, and achieves high-accuracy channel estimation, thereby alleviating both the underdetermined nature and computational overhead in IRS-assisted systems.

Abstract

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.

Problem

• Sparse Channel Estimation
• mmWave Near-Field Communications
• Underdetermined Problem
• Spherical Wavefront
• Meta-Surface (SIM)

Innovation

• polar-domain channel representation
• compressed sensing
• sparse Bayesian learning
• near-field communications
• intelligent meta-surface