Atomic Hybrid Sparse/Diffuse Channel Estimation and Cram\'er-Rao Bounds Analysis

TL;DR

This paper introduces a hybrid sparse/diffuse channel model and a novel estimation algorithm, HALS, supported by CRB analysis, to improve channel parameter estimation in frequency domain applications.

Contribution

It proposes the atomic hybrid sparse/diffuse (aHSD) model, develops the HALS algorithm with regularization, and provides theoretical CRB bounds for channel parameter estimation.

Findings

HALS effectively estimates sparse/diffuse channel components.

CRB analysis offers bounds that predict HALS performance.

Simulations validate the improved estimation accuracy.

Abstract

In this paper, an atomic hybrid sparse/diffuse (aHSD) channel model in the frequency domain is proposed. Based on a structural analysis of the resolvable paths and diffuse scattering statistics, the Hybrid Atomic-Least-Squares (HALS) algorithm is designed to estimate sparse/diffuse components with a combined atomic and $\ell_2$ regularization. A theoretical analysis of the Lagrange dual problem is conducted, and the conditions required for primal and dual solutions are provided, supporting an off-the-grid delay-time estimator. The Cram\'er--Rao Bound (CRB) analysis in this paper focuses on the estimation of the channel parameters, resulting in a bound on the aggregate channel. Lower and upper bounds for the CRB on parameters are derived as functions of the minimum separations between frequency parameters. Numerical results via simulations on synthetic and real data validate the efficacy of the HALS estimation strategy and show the improved predictive ability of the CRB analysis for the performance of HALS versus previously considered bounds.