Cooperative Spectrum Sensing Algorithm Based on CS-SLIM Iterative Minimization Sparse Learning
Dynamic spectrum management is a key technology in cognitive wireless sensor network (C-WSN), in which spectrum sensing plays an important role. In this paper, we propose an improved approach as sparse learning via iterative minimization based on compressive sampling (CS-SLIM) for wideband spectrum...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Hindawi - SAGE Publishing
2013-05-01
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| Series: | International Journal of Distributed Sensor Networks |
| Online Access: | https://doi.org/10.1155/2013/261953 |
| _version_ | 1827008940857098240 |
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| author | Ning Cao Xiaowen Hu Hao Lu Minghe Mao |
| author_facet | Ning Cao Xiaowen Hu Hao Lu Minghe Mao |
| author_sort | Ning Cao |
| collection | DOAJ |
| description | Dynamic spectrum management is a key technology in cognitive wireless sensor network (C-WSN), in which spectrum sensing plays an important role. In this paper, we propose an improved approach as sparse learning via iterative minimization based on compressive sampling (CS-SLIM) for wideband spectrum detection. CS-SLIM can provide wideband spectrum detection with almost the same accuracy but a lower computational burden than that of SLIM. The measurement matrix and the computational complexity for CS-SLIM are discussed. Mean squared errors (MSEs) at various measurement samples are provided to demonstrate the performance of the proposed approach in sparse scenes. It is also proved that the algorithm is suitable for sparse signal reconstruction and wideband spectrum sensing in C-WSN. |
| first_indexed | 2024-03-12T19:56:27Z |
| format | Article |
| id | doaj.art-ecd911f6efd44e9f976d3c8f137c82c2 |
| institution | Directory Open Access Journal |
| issn | 1550-1477 |
| language | English |
| last_indexed | 2025-02-18T12:39:47Z |
| publishDate | 2013-05-01 |
| publisher | Hindawi - SAGE Publishing |
| record_format | Article |
| series | International Journal of Distributed Sensor Networks |
| spelling | doaj.art-ecd911f6efd44e9f976d3c8f137c82c22024-11-02T04:12:15ZengHindawi - SAGE PublishingInternational Journal of Distributed Sensor Networks1550-14772013-05-01910.1155/2013/261953Cooperative Spectrum Sensing Algorithm Based on CS-SLIM Iterative Minimization Sparse LearningNing CaoXiaowen HuHao LuMinghe MaoDynamic spectrum management is a key technology in cognitive wireless sensor network (C-WSN), in which spectrum sensing plays an important role. In this paper, we propose an improved approach as sparse learning via iterative minimization based on compressive sampling (CS-SLIM) for wideband spectrum detection. CS-SLIM can provide wideband spectrum detection with almost the same accuracy but a lower computational burden than that of SLIM. The measurement matrix and the computational complexity for CS-SLIM are discussed. Mean squared errors (MSEs) at various measurement samples are provided to demonstrate the performance of the proposed approach in sparse scenes. It is also proved that the algorithm is suitable for sparse signal reconstruction and wideband spectrum sensing in C-WSN.https://doi.org/10.1155/2013/261953 |
| spellingShingle | Ning Cao Xiaowen Hu Hao Lu Minghe Mao Cooperative Spectrum Sensing Algorithm Based on CS-SLIM Iterative Minimization Sparse Learning International Journal of Distributed Sensor Networks |
| title | Cooperative Spectrum Sensing Algorithm Based on CS-SLIM Iterative Minimization Sparse Learning |
| title_full | Cooperative Spectrum Sensing Algorithm Based on CS-SLIM Iterative Minimization Sparse Learning |
| title_fullStr | Cooperative Spectrum Sensing Algorithm Based on CS-SLIM Iterative Minimization Sparse Learning |
| title_full_unstemmed | Cooperative Spectrum Sensing Algorithm Based on CS-SLIM Iterative Minimization Sparse Learning |
| title_short | Cooperative Spectrum Sensing Algorithm Based on CS-SLIM Iterative Minimization Sparse Learning |
| title_sort | cooperative spectrum sensing algorithm based on cs slim iterative minimization sparse learning |
| url | https://doi.org/10.1155/2013/261953 |
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