Hybrid Near-Far Field Channel Estimation for Holographic MIMO Communications

TL;DR

The paper tackles holographic MIMO with enlarged Fresnel regions, where users and scatterers lie in a hybrid near-far field. It identifies a power diffusion effect in transform domains that creates fake paths and degrades traditional estimation methods. To address this, it introduces PD-OMP, a PD-aware OMP algorithm that uses a joint angular-polar domain transform and a power diffusion range to reliably recover the hybrid-field channel without prior path counts. Theoretical analyses (complexity and CRLB) and extensive simulations show that PD-OMP achieves higher estimation accuracy with linear complexity and robustness to SNR, pilot length, and scatterer distribution, outperforming state-of-the-art hybrid-field methods.

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.

Figures (15)

• Figure 1: Hybrid channel model with a linear holographic antenna array.
• Figure 2: The angular-domain transform result of a hybrid-field channel consisting of a far-field and a near-field steering vector.
• Figure 3: The polar-domain transform result of a hybrid-field channel consisting of a far-field and a near-field steering vector.
• Figure 4: The incorrect path detection result using 2-iteration OMP based on $\mathbf{h}_{A,H}$. The error comes from the power diffusion effect.
• Figure 5: The incorrect path detection result using 2-iteration OMP based on $\mathbf{h}_{P,H}$. The error comes from the power diffusion effect.

Theorems & Definitions (4)

• proof
• proof
• proof
• proof