An adaptive combination constrained proportionate normalized maximum correntropy criterion algorithm for sparse channel estimations
Abstract An adaptive combination constrained proportionate normalized maximum correntropy criterion (ACC-PNMCC) algorithm is proposed for sparse multi-path channel estimation under mixed Gaussian noise environment. The developed ACC-PNMCC algorithm is implemented by incorporating an adaptive combina...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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SpringerOpen
2018-09-01
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| Series: | EURASIP Journal on Advances in Signal Processing |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1186/s13634-018-0581-5 |
| _version_ | 1828874222846345216 |
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| author | Yanyan Wang Yingsong Li José Carlos M. Bermudez Xiao Han |
| author_facet | Yanyan Wang Yingsong Li José Carlos M. Bermudez Xiao Han |
| author_sort | Yanyan Wang |
| collection | DOAJ |
| description | Abstract An adaptive combination constrained proportionate normalized maximum correntropy criterion (ACC-PNMCC) algorithm is proposed for sparse multi-path channel estimation under mixed Gaussian noise environment. The developed ACC-PNMCC algorithm is implemented by incorporating an adaptive combination function into the cost function of the proportionate normalized maximum correntropy criterion (PNMCC) algorithm to create a new penalty on the filter coefficients according to the devised threshold, which is based on the proportionate-type adaptive filter techniques and compressive sensing (CS) concept. The derivation of the proposed ACC-PNMCC algorithm is mathematically presented, and various simulation experiments have been carried out to investigate the performance of the proposed ACC-PNMCC algorithm. The experimental results show that our ACC-PNMCC algorithm outperforms the PNMCC and sparse PNMCC algorithms for sparse multi-path channel estimation applications. |
| first_indexed | 2024-12-13T07:33:01Z |
| format | Article |
| id | doaj.art-91adf405dbda43cf89a8d219ad2d3179 |
| institution | Directory Open Access Journal |
| issn | 1687-6180 |
| language | English |
| last_indexed | 2024-12-13T07:33:01Z |
| publishDate | 2018-09-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | EURASIP Journal on Advances in Signal Processing |
| spelling | doaj.art-91adf405dbda43cf89a8d219ad2d31792022-12-21T23:55:09ZengSpringerOpenEURASIP Journal on Advances in Signal Processing1687-61802018-09-012018111310.1186/s13634-018-0581-5An adaptive combination constrained proportionate normalized maximum correntropy criterion algorithm for sparse channel estimationsYanyan Wang0Yingsong Li1José Carlos M. Bermudez2Xiao Han3College of Information and Communication Engineering, Harbin Engineering UniversityCollege of Information and Communication Engineering, Harbin Engineering UniversityElectrical Engineering, Federal University of Santa Catarina (UFSC), FlorianópolisAcoustic Science and Technology Laboratory, Harbin Engineering UniversityAbstract An adaptive combination constrained proportionate normalized maximum correntropy criterion (ACC-PNMCC) algorithm is proposed for sparse multi-path channel estimation under mixed Gaussian noise environment. The developed ACC-PNMCC algorithm is implemented by incorporating an adaptive combination function into the cost function of the proportionate normalized maximum correntropy criterion (PNMCC) algorithm to create a new penalty on the filter coefficients according to the devised threshold, which is based on the proportionate-type adaptive filter techniques and compressive sensing (CS) concept. The derivation of the proposed ACC-PNMCC algorithm is mathematically presented, and various simulation experiments have been carried out to investigate the performance of the proposed ACC-PNMCC algorithm. The experimental results show that our ACC-PNMCC algorithm outperforms the PNMCC and sparse PNMCC algorithms for sparse multi-path channel estimation applications.http://link.springer.com/article/10.1186/s13634-018-0581-5Sparse PNMCC algorithmMixed Gaussian noise environmentZero-attracting techniqueAdaptive combination constraint |
| spellingShingle | Yanyan Wang Yingsong Li José Carlos M. Bermudez Xiao Han An adaptive combination constrained proportionate normalized maximum correntropy criterion algorithm for sparse channel estimations EURASIP Journal on Advances in Signal Processing Sparse PNMCC algorithm Mixed Gaussian noise environment Zero-attracting technique Adaptive combination constraint |
| title | An adaptive combination constrained proportionate normalized maximum correntropy criterion algorithm for sparse channel estimations |
| title_full | An adaptive combination constrained proportionate normalized maximum correntropy criterion algorithm for sparse channel estimations |
| title_fullStr | An adaptive combination constrained proportionate normalized maximum correntropy criterion algorithm for sparse channel estimations |
| title_full_unstemmed | An adaptive combination constrained proportionate normalized maximum correntropy criterion algorithm for sparse channel estimations |
| title_short | An adaptive combination constrained proportionate normalized maximum correntropy criterion algorithm for sparse channel estimations |
| title_sort | adaptive combination constrained proportionate normalized maximum correntropy criterion algorithm for sparse channel estimations |
| topic | Sparse PNMCC algorithm Mixed Gaussian noise environment Zero-attracting technique Adaptive combination constraint |
| url | http://link.springer.com/article/10.1186/s13634-018-0581-5 |
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