Compressive Near-Field Wideband Channel Estimation for THz Extremely Large-scale MIMO Systems

TL;DR

This paper introduces a novel compressive sensing approach for estimating wideband near-field THz MIMO channels, accounting for spherical wavefront and beam-splitting effects, with improved accuracy over traditional methods.

Contribution

It proposes a frequency-independent orthogonal dictionary and a 2D block-sparse model for efficient near-field wideband channel estimation in THz MIMO systems.

Findings

The proposed method outperforms conventional polar-domain approaches.

It effectively captures the near-field and wideband effects in channel modeling.

Numerical results validate the advantages of the 2D block-sparsity-aware framework.

Abstract

We consider the channel acquisition problem for a wideband terahertz (THz) communication system, where an extremely large-scale array is deployed to mitigate severe path attenuation. In channel modeling, we account for both the near-field spherical wavefront and the wideband beam-splitting phenomena, resulting in a wideband near-field channel. We propose a frequency-independent orthogonal dictionary that generalizes the standard discrete Fourier transform (DFT) matrix by introducing an additional parameter to capture the near-field property. This dictionary enables the wideband near-field channel to be efficiently represented with a two-dimensional (2D) block-sparse structure. Leveraging this specific sparse structure, the wideband near-field channel estimation problem can be effectively addressed within a customized compressive sensing framework. Numerical results demonstrate the significant advantages of our proposed 2D block-sparsity-aware method over conventional polar-domain-based approaches for near-field wideband channel estimation.