Stochastic Block NIHT Algorithm for Adaptive Block-Sparse System Identification

Compressive sensing (CS) theory has been widely used in various fields, such as wireless communications. One of the main issues in the wireless communication field in recent years is how to identify block-sparse systems. We can follow this issue, by using CS theory and block-sparse signal recovery algorithms.

This paper presents a new block-sparse signal recovery algorithm for the adaptive block-sparse system identification scenario, named stochastic block normalized iterative hard thresholding (SBNIHT) algorithm. The proposed algorithm is a new block version of the SSR normalized iterative hard thresholding (NIHT) algorithm with an adaptive filter framework. It uses a search method to identify the blocks of the impulse response of the unknown block-sparse system that we wish to estimate. In addition, the necessary condition to guarantee the convergence for this algorithm is derived in this paper.

Simulation results show that the proposed SBNIHT algorithm has a better performance than other algorithms in the literature with respect to the convergence and tracking capability.

In this study, one new greedy algorithm is suggested for the block-sparse system identification scenario. Although the proposed SBNIHT algorithm is more complex than other competing algorithms but has better convergence and tracking capability performance. The author(s). This is an open access article distributed under the terms of the Creative Commons Attribution (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, as long as the original authors and source are cited. No permission is required from the authors or the publishers.