Atomic Hybrid Sparse/Diffuse Channel Estimation and Cramér-Rao Bounds Analysis
This work provides a new estimation method and theoretical bounds for channel estimation in wireless communications, which is an incremental improvement over existing approaches.
The paper proposes an atomic hybrid sparse/diffuse (aHSD) channel model and a Hybrid Atomic-Least-Squares (HALS) algorithm for estimating sparse/diffuse components. Numerical results show improved predictive ability of the Cramér-Rao Bound analysis for HALS performance compared to previous bounds.
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 Cramér--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.