Parametric Near-Field Channel Estimation for Extremely Large Aperture Arrays

TL;DR

This paper introduces a parametric near-field channel estimation method for extremely large aperture arrays, leveraging MUSIC and least-squares techniques to improve accuracy in near-field conditions.

Contribution

It presents a novel multi-user near-field channel estimation algorithm that incorporates near-field effects and enhances estimation accuracy over classical methods.

Findings

Outperforms classical least-squares in beamforming gain

Achieves lower mean-square error in channel estimation

Effective in near-field propagation scenarios

Abstract

Accurate channel estimation is critical to fully exploit the beamforming gains when communicating with extremely large aperture arrays. The propagation distances between the user and receiver, which potentially has thousands of antennas/elements, are such that they are located in the radiative near-field region of each other when considering the Fraunhofer distance of the entire array. Therefore, it is imperative to consider near-field effects to achieve proper channel estimation. This paper proposes a parametric multi-user near-field channel estimation algorithm based on MUltiple SIgnal Classification (MUSIC) method to obtain the essential parameters describing the users' locations. We derive the estimated channel by incorporating the estimated parameters into the near-field channel model. Additionally, we implement a least-squares-based estimation corrector, resulting in a precise near-field channel estimation. Simulation results demonstrate that our proposed scheme outperforms classical least-squares and minimum mean-square error channel estimation methods in terms of normalized beamforming gain and normalized mean-square error.