Estimation algorithm for sparse channels with gradient guided p-norm like constraints
The l<sub>0</sub>and l<sub>1</sub>norm constrained least mean square (LMS) algorithm can effectively improve the performance of the sparse channel estimation, but the convergence performance of such algorithms will considerably vary when the channel exhibits different sparisi...
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| Format: | Article |
| Language: | zho |
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Editorial Department of Journal on Communications
2014-07-01
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| Series: | Tongxin xuebao |
| Subjects: | |
| Online Access: | http://www.joconline.com.cn/zh/article/doi/10.3969/j.issn.1000-436x.2014.07.021/ |
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| _version_ | 1841539740836298752 |
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| author | Fei-yun WU Yue-hai ZHOU Feng TONG |
| author_facet | Fei-yun WU Yue-hai ZHOU Feng TONG |
| author_sort | Fei-yun WU |
| collection | DOAJ |
| description | The l<sub>0</sub>and l<sub>1</sub>norm constrained least mean square (LMS) algorithm can effectively improve the performance of the sparse channel estimation, but the convergence performance of such algorithms will considerably vary when the channel exhibits different sparisity. A novel p-norm like constraint LMS algorithm to accommodate the various sparisity of the channels through the introducing of the variable p-value was presented. Furthermore, the gradient guided optimiza-tion of the p-value was derived. Numerical simulation results are given to demonstrate the superiority of the new algorithm. |
| format | Article |
| id | doaj-art-797510bd6045485191f99d5acf7c51f8 |
| institution | Kabale University |
| issn | 1000-436X |
| language | zho |
| publishDate | 2014-07-01 |
| publisher | Editorial Department of Journal on Communications |
| record_format | Article |
| series | Tongxin xuebao |
| spelling | doaj-art-797510bd6045485191f99d5acf7c51f82025-01-14T06:43:51ZzhoEditorial Department of Journal on CommunicationsTongxin xuebao1000-436X2014-07-013517217759683023Estimation algorithm for sparse channels with gradient guided p-norm like constraintsFei-yun WUYue-hai ZHOUFeng TONGThe l<sub>0</sub>and l<sub>1</sub>norm constrained least mean square (LMS) algorithm can effectively improve the performance of the sparse channel estimation, but the convergence performance of such algorithms will considerably vary when the channel exhibits different sparisity. A novel p-norm like constraint LMS algorithm to accommodate the various sparisity of the channels through the introducing of the variable p-value was presented. Furthermore, the gradient guided optimiza-tion of the p-value was derived. Numerical simulation results are given to demonstrate the superiority of the new algorithm.http://www.joconline.com.cn/zh/article/doi/10.3969/j.issn.1000-436x.2014.07.021/p-norm like constraintLMS algorithmsparse channels |
| spellingShingle | Fei-yun WU Yue-hai ZHOU Feng TONG Estimation algorithm for sparse channels with gradient guided p-norm like constraints Tongxin xuebao p-norm like constraint LMS algorithm sparse channels |
| title | Estimation algorithm for sparse channels with gradient guided p-norm like constraints |
| title_full | Estimation algorithm for sparse channels with gradient guided p-norm like constraints |
| title_fullStr | Estimation algorithm for sparse channels with gradient guided p-norm like constraints |
| title_full_unstemmed | Estimation algorithm for sparse channels with gradient guided p-norm like constraints |
| title_short | Estimation algorithm for sparse channels with gradient guided p-norm like constraints |
| title_sort | estimation algorithm for sparse channels with gradient guided p norm like constraints |
| topic | p-norm like constraint LMS algorithm sparse channels |
| url | http://www.joconline.com.cn/zh/article/doi/10.3969/j.issn.1000-436x.2014.07.021/ |
| work_keys_str_mv | AT feiyunwu estimationalgorithmforsparsechannelswithgradientguidedpnormlikeconstraints AT yuehaizhou estimationalgorithmforsparsechannelswithgradientguidedpnormlikeconstraints AT fengtong estimationalgorithmforsparsechannelswithgradientguidedpnormlikeconstraints |