ISI sparse channel estimation based on ISL0 algorithm
A smoothed L0 (SL0) algorithm based on compressed sensing proposed in previous works for inter symbol in-terference (ISI) sparse channel estimation. But this method has “notched effect” due to the negative iterative gradient direction. Moreover, the “steep nature” of cost function in SL0 is not stee...
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| Format: | Article |
| Language: | zho |
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Editorial Department of Journal on Communications
2014-05-01
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| Series: | Tongxin xuebao |
| Subjects: | |
| Online Access: | http://www.joconline.com.cn/zh/article/doi/10.3969/j.issn.1000-436x.2014.05.017/ |
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| _version_ | 1841539713110900736 |
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| author | Ting LIU Jie ZHOU CHIJiu-he JU |
| author_facet | Ting LIU Jie ZHOU CHIJiu-he JU |
| author_sort | Ting LIU |
| collection | DOAJ |
| description | A smoothed L0 (SL0) algorithm based on compressed sensing proposed in previous works for inter symbol in-terference (ISI) sparse channel estimation. But this method has “notched effect” due to the negative iterative gradient direction. Moreover, the “steep nature” of cost function in SL0 is not steep enough, leading to channel estimation errors and make convergence results not the most optimal. The lagrange multipliers and newton method were combined to op-timize SL0 algorithm in order to obtain a more rapid and efficient signal reconstruction algorithm termed as an improved smoothed L0 (ISL0). The channel state information (CSI) of the sparse multi-path channel was obtained and analysis of reconstructed signal deviation, mean squared error (MSE) in the perspective of iterations and signal-to-noise ratio (SNR) as well as the iteration time and ISI equalization performance were also done. Furthermore, the superiority of ISL0 has been verified by computer simulation. Real-time simulation results clearly show that the ISL0 algorithm can estimate the ISI sparse channel much better. Compared with CoSaMP, SL0 and some other algorithms, the ISL0 algorithm can greatly improve the performance of system in the same channel environments. |
| format | Article |
| id | doaj-art-227d012731944f07a4738900f244f68b |
| institution | Kabale University |
| issn | 1000-436X |
| language | zho |
| publishDate | 2014-05-01 |
| publisher | Editorial Department of Journal on Communications |
| record_format | Article |
| series | Tongxin xuebao |
| spelling | doaj-art-227d012731944f07a4738900f244f68b2025-01-14T06:43:24ZzhoEditorial Department of Journal on CommunicationsTongxin xuebao1000-436X2014-05-013512413359681627ISI sparse channel estimation based on ISL0 algorithmTing LIUJie ZHOUCHIJiu-he JUA smoothed L0 (SL0) algorithm based on compressed sensing proposed in previous works for inter symbol in-terference (ISI) sparse channel estimation. But this method has “notched effect” due to the negative iterative gradient direction. Moreover, the “steep nature” of cost function in SL0 is not steep enough, leading to channel estimation errors and make convergence results not the most optimal. The lagrange multipliers and newton method were combined to op-timize SL0 algorithm in order to obtain a more rapid and efficient signal reconstruction algorithm termed as an improved smoothed L0 (ISL0). The channel state information (CSI) of the sparse multi-path channel was obtained and analysis of reconstructed signal deviation, mean squared error (MSE) in the perspective of iterations and signal-to-noise ratio (SNR) as well as the iteration time and ISI equalization performance were also done. Furthermore, the superiority of ISL0 has been verified by computer simulation. Real-time simulation results clearly show that the ISL0 algorithm can estimate the ISI sparse channel much better. Compared with CoSaMP, SL0 and some other algorithms, the ISL0 algorithm can greatly improve the performance of system in the same channel environments.http://www.joconline.com.cn/zh/article/doi/10.3969/j.issn.1000-436x.2014.05.017/compressed-sensinglinear programnon-convex optimizationISL0 algorithmsparse restoration |
| spellingShingle | Ting LIU Jie ZHOU CHIJiu-he JU ISI sparse channel estimation based on ISL0 algorithm Tongxin xuebao compressed-sensing linear program non-convex optimization ISL0 algorithm sparse restoration |
| title | ISI sparse channel estimation based on ISL0 algorithm |
| title_full | ISI sparse channel estimation based on ISL0 algorithm |
| title_fullStr | ISI sparse channel estimation based on ISL0 algorithm |
| title_full_unstemmed | ISI sparse channel estimation based on ISL0 algorithm |
| title_short | ISI sparse channel estimation based on ISL0 algorithm |
| title_sort | isi sparse channel estimation based on isl0 algorithm |
| topic | compressed-sensing linear program non-convex optimization ISL0 algorithm sparse restoration |
| url | http://www.joconline.com.cn/zh/article/doi/10.3969/j.issn.1000-436x.2014.05.017/ |
| work_keys_str_mv | AT tingliu isisparsechannelestimationbasedonisl0algorithm AT jiezhou isisparsechannelestimationbasedonisl0algorithm AT chijiuheju isisparsechannelestimationbasedonisl0algorithm |