Pilot design for underwater MIMO cosparse channel estimation based on compressed sensing
In multiple-input multiple-output–orthogonal frequency-division multiplexing underwater acoustic communication systems, the correlation of the sampling matrix is the key of the channel estimation algorithm based on compressed sensing. To reduce the cross-correlation of the sampling matrix and improv...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2021-06-01
|
| Series: | International Journal of Distributed Sensor Networks |
| Online Access: | https://doi.org/10.1177/15501477211017825 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850222236437315584 |
|---|---|
| author | Yun Li Lingxia Liao Shanlin Sun Zhicheng Tan Xing Yao |
| author_facet | Yun Li Lingxia Liao Shanlin Sun Zhicheng Tan Xing Yao |
| author_sort | Yun Li |
| collection | DOAJ |
| description | In multiple-input multiple-output–orthogonal frequency-division multiplexing underwater acoustic communication systems, the correlation of the sampling matrix is the key of the channel estimation algorithm based on compressed sensing. To reduce the cross-correlation of the sampling matrix and improve the channel estimation performance, a pilot design algorithm for co-sparse channel estimation based on compressed sensing is proposed in this article. Based on the time-domain correlation of the channel, the channel estimation is modeled as a common sparse signal reconstruction problem. When replacing each pilot indices position, the algorithm selects multiple pilot indices with the least cross-correlation from the alternative positions to replace the current pilot indices position, and it uses the inner and outer two-layer loops to realize the bit-by-bit optimal replacement of the pilot. The simulation results show that the channel estimation mean squared error of pilot design algorithm for co-sparse channel estimation based on compressed sensing can be reduced by approximately 18 dB compared with the least square algorithm. Compared with the genetic algorithm and search space size methods, the structural sequence search proposed by pilot design algorithm for co-sparse channel estimation based on compressed sensing is used to design the pilot to complete the channel estimation. Thus, the mean squared error of the channel estimation can be reduced by 2 dB. At the same bit error rate of 0.03, the signal-to-noise ratio can be decreased by approximately 7 dB. |
| format | Article |
| id | doaj-art-72c2ab048b914650a5bd39df8404f82b |
| institution | OA Journals |
| issn | 1550-1477 |
| language | English |
| publishDate | 2021-06-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Distributed Sensor Networks |
| spelling | doaj-art-72c2ab048b914650a5bd39df8404f82b2025-08-20T02:06:26ZengWileyInternational Journal of Distributed Sensor Networks1550-14772021-06-011710.1177/15501477211017825Pilot design for underwater MIMO cosparse channel estimation based on compressed sensingYun Li0Lingxia Liao1Shanlin Sun2Zhicheng Tan3Xing Yao4School of Information and Statistics, Guangxi University of Finance and Economics, Nanning, ChinaCollege of Electronic Information and Automation, Guilin University of Aerospace Technology, Guilin, ChinaCollege of Electronic Information and Automation, Guilin University of Aerospace Technology, Guilin, ChinaCollege of Electronic Information and Automation, Guilin University of Aerospace Technology, Guilin, ChinaCollege of Electronic Information and Automation, Guilin University of Aerospace Technology, Guilin, ChinaIn multiple-input multiple-output–orthogonal frequency-division multiplexing underwater acoustic communication systems, the correlation of the sampling matrix is the key of the channel estimation algorithm based on compressed sensing. To reduce the cross-correlation of the sampling matrix and improve the channel estimation performance, a pilot design algorithm for co-sparse channel estimation based on compressed sensing is proposed in this article. Based on the time-domain correlation of the channel, the channel estimation is modeled as a common sparse signal reconstruction problem. When replacing each pilot indices position, the algorithm selects multiple pilot indices with the least cross-correlation from the alternative positions to replace the current pilot indices position, and it uses the inner and outer two-layer loops to realize the bit-by-bit optimal replacement of the pilot. The simulation results show that the channel estimation mean squared error of pilot design algorithm for co-sparse channel estimation based on compressed sensing can be reduced by approximately 18 dB compared with the least square algorithm. Compared with the genetic algorithm and search space size methods, the structural sequence search proposed by pilot design algorithm for co-sparse channel estimation based on compressed sensing is used to design the pilot to complete the channel estimation. Thus, the mean squared error of the channel estimation can be reduced by 2 dB. At the same bit error rate of 0.03, the signal-to-noise ratio can be decreased by approximately 7 dB.https://doi.org/10.1177/15501477211017825 |
| spellingShingle | Yun Li Lingxia Liao Shanlin Sun Zhicheng Tan Xing Yao Pilot design for underwater MIMO cosparse channel estimation based on compressed sensing International Journal of Distributed Sensor Networks |
| title | Pilot design for underwater MIMO cosparse channel estimation based on compressed sensing |
| title_full | Pilot design for underwater MIMO cosparse channel estimation based on compressed sensing |
| title_fullStr | Pilot design for underwater MIMO cosparse channel estimation based on compressed sensing |
| title_full_unstemmed | Pilot design for underwater MIMO cosparse channel estimation based on compressed sensing |
| title_short | Pilot design for underwater MIMO cosparse channel estimation based on compressed sensing |
| title_sort | pilot design for underwater mimo cosparse channel estimation based on compressed sensing |
| url | https://doi.org/10.1177/15501477211017825 |
| work_keys_str_mv | AT yunli pilotdesignforunderwatermimocosparsechannelestimationbasedoncompressedsensing AT lingxialiao pilotdesignforunderwatermimocosparsechannelestimationbasedoncompressedsensing AT shanlinsun pilotdesignforunderwatermimocosparsechannelestimationbasedoncompressedsensing AT zhichengtan pilotdesignforunderwatermimocosparsechannelestimationbasedoncompressedsensing AT xingyao pilotdesignforunderwatermimocosparsechannelestimationbasedoncompressedsensing |