Beyond Spherical Wavefront: Near-Field Channel Estimation Under Wavefront Anisotropy

TL;DR

This paper introduces a new anisotropic wavefront channel model for near-field estimation in large aperture arrays, addressing inaccuracies caused by wavefront anisotropy from curved reflecting surfaces.

Contribution

It formulates a parameterized anisotropic wavefront channel model and proposes a physical parameter recovery-based estimation algorithm for it.

Findings

AWC model captures wavefront anisotropy effects.

Simulation shows AWC lacks sparsity in angle-distance domain.

Algorithm effectively estimates channels with anisotropic wavefronts.

Abstract

Extremely large aperture arrays (ELAAs) and millimeter-wave (mmWave) technologies are essential for achieving high data rates in future wireless communication systems. To perform precise beamforming, these systems require accurate channel estimation, in which the near-field wavefront curvature effect must be taken into account. Existing channel estimation methods rely on the spherical wavefront channel (SWC) model, which is suitable for near-field propagation with point sources, scatterers, and reflection planes. However, when a near-field curved reflecting surface exists, the wavefront of the reflected wave becomes anisotropic rather than spherical, causing the SWC model to become inaccurate. To address this problem, in this paper, we formulate a parameterized model for the anisotropic wavefront channel (AWC). Using this model, we propose a channel estimation algorithm based on physical parameter recovery for the AWC. Simulation results reveal that the AWC no longer retains sparsity in the angle-distance domain. Furthermore, the results demonstrate how different physical characteristics of the propagation scenario affect the degree of wavefront anisotropy, and confirm the effectiveness of our proposed algorithm in AWC scenarios.