Reweighted Factor Selection for SLMS-RL1 Algorithm under Gaussian Mixture Noise Environments
The sign least mean square with reweighted L1-norm constraint (SLMS-RL1) algorithm is an attractive sparse channel estimation method among Gaussian mixture model (GMM) based algorithms for use in impulsive noise environments. The channel sparsity can be exploited by SLMS-RL1 algorithm based on appro...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2015-09-01
|
| Series: | Algorithms |
| Subjects: | |
| Online Access: | http://www.mdpi.com/1999-4893/8/4/799 |
| _version_ | 1828400048506929152 |
|---|---|
| author | Tingping Zhang Guan Gui |
| author_facet | Tingping Zhang Guan Gui |
| author_sort | Tingping Zhang |
| collection | DOAJ |
| description | The sign least mean square with reweighted L1-norm constraint (SLMS-RL1) algorithm is an attractive sparse channel estimation method among Gaussian mixture model (GMM) based algorithms for use in impulsive noise environments. The channel sparsity can be exploited by SLMS-RL1 algorithm based on appropriate reweighted factor, which is one of key parameters to adjust the sparse constraint for SLMS-RL1 algorithm. However, to the best of the authors’ knowledge, a reweighted factor selection scheme has not been developed. This paper proposes a Monte-Carlo (MC) based reweighted factor selection method to further strengthen the performance of SLMS-RL1 algorithm. To validate the performance of SLMS-RL1 using the proposed reweighted factor, simulations results are provided to demonstrate that convergence speed can be reduced by increasing the channel sparsity, while the steady-state MSE performance only slightly changes with different GMM impulsive-noise strengths. |
| first_indexed | 2024-12-10T09:25:44Z |
| format | Article |
| id | doaj.art-700470398c9e45be8c0283443e2094de |
| institution | Directory Open Access Journal |
| issn | 1999-4893 |
| language | English |
| last_indexed | 2024-12-10T09:25:44Z |
| publishDate | 2015-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Algorithms |
| spelling | doaj.art-700470398c9e45be8c0283443e2094de2022-12-22T01:54:32ZengMDPI AGAlgorithms1999-48932015-09-018479980910.3390/a8040799a8040799Reweighted Factor Selection for SLMS-RL1 Algorithm under Gaussian Mixture Noise EnvironmentsTingping Zhang0Guan Gui1School of Information Science and Engineering, Chongqing Jiaotong University, Chongqing 400074, ChinaDepartment Electronics and Information Systems, Akita Prefectural University, Akita 015-0055, JapanThe sign least mean square with reweighted L1-norm constraint (SLMS-RL1) algorithm is an attractive sparse channel estimation method among Gaussian mixture model (GMM) based algorithms for use in impulsive noise environments. The channel sparsity can be exploited by SLMS-RL1 algorithm based on appropriate reweighted factor, which is one of key parameters to adjust the sparse constraint for SLMS-RL1 algorithm. However, to the best of the authors’ knowledge, a reweighted factor selection scheme has not been developed. This paper proposes a Monte-Carlo (MC) based reweighted factor selection method to further strengthen the performance of SLMS-RL1 algorithm. To validate the performance of SLMS-RL1 using the proposed reweighted factor, simulations results are provided to demonstrate that convergence speed can be reduced by increasing the channel sparsity, while the steady-state MSE performance only slightly changes with different GMM impulsive-noise strengths.http://www.mdpi.com/1999-4893/8/4/799Sign least mean square (SLMS)reweighted L1-norm (RL1)reweighted factor selectionGaussian mixture model (GMM)sparse channel estimation |
| spellingShingle | Tingping Zhang Guan Gui Reweighted Factor Selection for SLMS-RL1 Algorithm under Gaussian Mixture Noise Environments Algorithms Sign least mean square (SLMS) reweighted L1-norm (RL1) reweighted factor selection Gaussian mixture model (GMM) sparse channel estimation |
| title | Reweighted Factor Selection for SLMS-RL1 Algorithm under Gaussian Mixture Noise Environments |
| title_full | Reweighted Factor Selection for SLMS-RL1 Algorithm under Gaussian Mixture Noise Environments |
| title_fullStr | Reweighted Factor Selection for SLMS-RL1 Algorithm under Gaussian Mixture Noise Environments |
| title_full_unstemmed | Reweighted Factor Selection for SLMS-RL1 Algorithm under Gaussian Mixture Noise Environments |
| title_short | Reweighted Factor Selection for SLMS-RL1 Algorithm under Gaussian Mixture Noise Environments |
| title_sort | reweighted factor selection for slms rl1 algorithm under gaussian mixture noise environments |
| topic | Sign least mean square (SLMS) reweighted L1-norm (RL1) reweighted factor selection Gaussian mixture model (GMM) sparse channel estimation |
| url | http://www.mdpi.com/1999-4893/8/4/799 |
| work_keys_str_mv | AT tingpingzhang reweightedfactorselectionforslmsrl1algorithmundergaussianmixturenoiseenvironments AT guangui reweightedfactorselectionforslmsrl1algorithmundergaussianmixturenoiseenvironments |