Near/Far-Field Channel Estimation For Terahertz Systems With ELAAs: A Block-Sparse-Aware Approach

TL;DR

This paper introduces a block-sparse-aware method for hybrid near/far-field channel estimation in Terahertz systems with extremely large-scale antenna arrays, accounting for spherical wave propagation effects.

Contribution

It models the near/far-field channel as a block-sparse signal on an orthogonal dictionary, enabling improved estimation in ELAAs with theoretical analysis and simulation validation.

Findings

Block-sparse representation percentage decreases as 1/√N with more antennas.

Proposed method outperforms existing state-of-the-art channel estimation techniques.

Theoretical analysis confirms the validity of the block-sparse model for near/far-field channels.

Abstract

Millimeter wave/Terahertz (mmWave/THz) communication with extremely large-scale antenna arrays (ELAAs) offers a promising solution to meet the escalating demand for high data rates in next-generation communications. A large array aperture, along with the ever increasing carrier frequency within the mmWave/THz bands, leads to a large Rayleigh distance. As a result, the traditional plane-wave assumption may not hold valid for mmWave/THz systems featuring ELAAs. In this paper, we consider the problem of hybrid near/far-field channel estimation by taking spherical wave propagation into account. By analyzing the coherence properties of any two near-field steering vectors, we prove that the hybrid near/far-field channel admits a block-sparse representation on a specially designed orthogonal dictionary. Specifically, the percentage of nonzero elements of such a block-sparse representation decreases in the order of $1/\sqrt{N}$, which tends to zero as the number of antennas, $N$, grows. Such a block-sparse representation allows to convert channel estimation into a block-sparse signal recovery problem. Simulation results are provided to verify our theoretical results and illustrate the performance of the proposed channel estimation approach in comparison with existing state-of-the-art methods.