Stochastic Block NIHT Algorithm for Adaptive Block-Sparse System Identification
Background and Objectives: 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 blo...
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| Format: | Article |
| Language: | English |
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Shahid Rajaee Teacher Training University
2021-01-01
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| Series: | Journal of Electrical and Computer Engineering Innovations |
| Subjects: | |
| Online Access: | https://jecei.sru.ac.ir/article_1492_bd95c05d5f27de69609ec61bc65fe03c.pdf |
| _version_ | 1818200993443086336 |
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| author | Z. Habibi H. Zayyani M. Shams Esfandabadi |
| author_facet | Z. Habibi H. Zayyani M. Shams Esfandabadi |
| author_sort | Z. Habibi |
| collection | DOAJ |
| description | Background and Objectives: 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.Methods: 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.Results: 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.Conclusion: 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. |
| first_indexed | 2024-12-12T02:46:29Z |
| format | Article |
| id | doaj.art-6b638c205bc44397a7b9a0218518d4b1 |
| institution | Directory Open Access Journal |
| issn | 2322-3952 2345-3044 |
| language | English |
| last_indexed | 2024-12-12T02:46:29Z |
| publishDate | 2021-01-01 |
| publisher | Shahid Rajaee Teacher Training University |
| record_format | Article |
| series | Journal of Electrical and Computer Engineering Innovations |
| spelling | doaj.art-6b638c205bc44397a7b9a0218518d4b12022-12-22T00:41:02ZengShahid Rajaee Teacher Training UniversityJournal of Electrical and Computer Engineering Innovations2322-39522345-30442021-01-019111512610.22061/jecei.2020.7525.4011492Stochastic Block NIHT Algorithm for Adaptive Block-Sparse System IdentificationZ. Habibi0H. Zayyani1M. Shams Esfandabadi2Research Institute for Information and Communications Technologies, Academic Center for Education, Culture and Research, Tehran, Iran.Faculty of Electrical and Computer Engineering, Qom University of Technology, Qom, Iran.Electronics Engineering Department, Faculty of Electrical Engineering, Shahid Rajaee Teacher Training University, Tehran, Iran.Background and Objectives: 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.Methods: 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.Results: 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.Conclusion: 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.https://jecei.sru.ac.ir/article_1492_bd95c05d5f27de69609ec61bc65fe03c.pdfcompressive sensing (cs)system identificationblock-sparse systemadaptive filter |
| spellingShingle | Z. Habibi H. Zayyani M. Shams Esfandabadi Stochastic Block NIHT Algorithm for Adaptive Block-Sparse System Identification Journal of Electrical and Computer Engineering Innovations compressive sensing (cs) system identification block-sparse system adaptive filter |
| title | Stochastic Block NIHT Algorithm for Adaptive Block-Sparse System Identification |
| title_full | Stochastic Block NIHT Algorithm for Adaptive Block-Sparse System Identification |
| title_fullStr | Stochastic Block NIHT Algorithm for Adaptive Block-Sparse System Identification |
| title_full_unstemmed | Stochastic Block NIHT Algorithm for Adaptive Block-Sparse System Identification |
| title_short | Stochastic Block NIHT Algorithm for Adaptive Block-Sparse System Identification |
| title_sort | stochastic block niht algorithm for adaptive block sparse system identification |
| topic | compressive sensing (cs) system identification block-sparse system adaptive filter |
| url | https://jecei.sru.ac.ir/article_1492_bd95c05d5f27de69609ec61bc65fe03c.pdf |
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