Cramér-Rao Bounds on Sparse-Diffuse Channel Estimation
Abstract
Lower bounds on the estimation of a hybrid sparse-diffuse channel and parameters of the channel are provided. The Hybrid Atomic-Least-Squares (HALS) algorithm was designed to jointly estimate the sparse and diffuse components with a combined atomic and ℓ2regularization for this hybrid, mixed-channel model. The Cramér-Rao Bound analysis in this paper 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 HALS estimation strategy and show the improved predictive ability of the CRB analysis for the performance of HALS versus previously considered bounds.
Fields of Study & Subjects
Statistical Methods and Inference
Advanced Wireless Communication Techniques
Publication details
- Journal / source
- ICASSP 2026 - 2026 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
- Publisher
- IEEE
- Pages
- 21526–21530
- Publication date
- 21 April 2026
- Language
- EN