Cramér-Rao Bounds on Sparse-Diffuse Channel Estimation
Résumé
An improved bound on the performance of a hybrid sparsediffuse atomic norm channel estimator is provided. The Hybrid Atomic-Least-Squares (HALS) algorithm was designed to jointly estimate the sparse and diffuse components with a combined atomic and ℓ 2 regularization for a mixed channel model. The improved Cramér-Rao Bound analysis focuses on the estimation of the channel parameters, resulting in a bound on the aggregate channel. Numerical results via simulations on synthetic data validate the efficacy of the proposed method and show the improved predictive ability of the new CRB analysis for the performance of HALS.
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