Hybrid Near-Far Field Channel Estimation for Holographic MIMO Communications

TL;DR

This paper introduces a novel channel estimation method for holographic MIMO systems that effectively addresses the hybrid near-far field challenge caused by the power diffusion effect, improving accuracy over existing techniques.

Contribution

It proposes a PD-aware orthogonal matching pursuit algorithm that accounts for the power diffusion effect in hybrid near-far field channel estimation, without prior knowledge of path numbers or user location.

Findings

PD-OMP outperforms existing hybrid-field estimation methods.

The method accurately identifies the PD range to eliminate fake paths.

Theoretical bounds and complexity analysis support the proposed approach.

Abstract

Holographic MIMO communications, enabled by large-scale antenna arrays with quasi-continuous apertures, is a potential technology for spectrum efficiency improvement. However, the increased antenna aperture size extends the range of the Fresnel region, leading to a hybrid near-far field communication mode. The users and scatterers randomly lie in near-field and far-field zones, and thus, conventional far-field-only and near-field-only channel estimation methods may not work. To tackle this challenge, we demonstrate the existence of the power diffusion (PD) effect, which leads to a mismatch between the hybrid-field channel and existing channel estimation methods. Specifically, in far-field and near-field transform domains, the power gain of one channel path may diffuse to other positions, thus generating fake paths. This renders the conventional techniques unable to detect those real paths. We propose a PD-aware orthogonal matching pursuit algorithm to eliminate the influence of the PD effect by identifying the PD range within which paths diffuse to other positions. PD-OMP fits a general case without prior knowledge of near-field and far-field path numbers and the user's location. The computational complexity of PD-OMP and the Cramer-Rao Lower Bound for the sparse-signal-recovery-based channel estimation are also derived. Simulation results show that PD-OMP outperforms state-of-the-art hybrid-field channel estimation methods.