Angular-Distance Based Channel Estimation for Holographic MIMO

TL;DR

This paper introduces a novel channel estimation method for holographic MIMO that accurately estimates 3D angles and distances by decomposing parameters and applying a compressive sensing framework, outperforming existing benchmarks.

Contribution

It proposes a parametric decomposition and compressed deconstruction framework with a new DeRe-VM algorithm for efficient 3D AED parameter detection in holographic MIMO channels.

Findings

Robust channel estimation across diverse conditions

Enhanced accuracy in 3D parameter recovery

Superior performance compared to state-of-the-art methods

Abstract

This paper investigates the channel estimation for holographic MIMO systems by unmasking their distinctions from the conventional one. Specifically, we elucidate that the channel estimation, subject to holographic MIMO's electromagnetically large antenna arrays, has to discriminate not only the angles of a user/scatterer but also its distance information, namely the three-dimensional (3D) azimuth and elevation angles plus the distance (AED) parameters. As the angular-domain representation fails to characterize the sparsity inherent in holographic MIMO channels, the tightly coupled 3D AED parameters are firstly decomposed for independently constructing their own covariance matrices. Then, the recovery of each individual parameter can be structured as a compressive sensing (CS) problem by harnessing the covariance matrix constructed. This pair of techniques contribute to a parametric decomposition and compressed deconstruction (DeRe) framework, along with a formulation of the maximum likelihood estimation for each parameter. Then, an efficient algorithm, namely DeRe-based variational Bayesian inference and message passing (DeRe-VM), is proposed for the sharp detection of the 3D AED parameters and the robust recovery of sparse channels. Finally, the proposed channel estimation regime is confirmed to be of great robustness in accommodating different channel conditions, regardless of the near-field and far-field contexts of a holographic MIMO system, as well as an improved performance in comparison to the state-of-the-art benchmarks.