Cramér–Rao Bounds on Sparse-Diffuse Channel Estimation

Lei Lyu, Maxime Ferreira Da Costa, and Urbashi Mitra

IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2026).

Abstract

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.

Cite

@inproceedings{lyu2025CramerraoBounds,
  author    = {Lyu, Lei and Ferreira Da Costa, Maxime and Mitra, Urbashi},
  title     = {{Cramér--Rao} Bounds on Sparse-Diffuse Channel Estimation},
  booktitle = {2026 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
  year      = {2026},
  doi       = {10.1109/ICASSP55912.2026.11463562}
}