An efficient spectrum sensing of mixed kernel SVM based on sampling covariance matrix
In recent years,with the blind detection algorithms were proposed,more and more blind algorithms based on sampling covariance matrix were applied to spectrum sensing.The detection threshold was an approximation,and the detection performance would be affected for this algorithms.Thus,the mixed kernel...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | zho |
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Beijing Xintong Media Co., Ltd
2019-11-01
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| Series: | Dianxin kexue |
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| Online Access: | http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2019210/ |
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| _version_ | 1841530619578810368 |
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| author | Jianyuan NIE Jianrong BAO Bin JIANG Chao LIU Fang ZHU Jianhai HE |
| author_facet | Jianyuan NIE Jianrong BAO Bin JIANG Chao LIU Fang ZHU Jianhai HE |
| author_sort | Jianyuan NIE |
| collection | DOAJ |
| description | In recent years,with the blind detection algorithms were proposed,more and more blind algorithms based on sampling covariance matrix were applied to spectrum sensing.The detection threshold was an approximation,and the detection performance would be affected for this algorithms.Thus,the mixed kernel function support vector machine (SVM) efficient spectrum sensing based on sampling covariance matrix was proposed.The statistics which were maximum minimum eigenvalue (MME) and covariance absolute value (CAV) of sensing signal sampling covariance matrices were used as the feature vectors of SVM and were trained to generate a spectrum sensing classifier.The advantage of this algorithm was that it needn’t calculate the detection threshold and the extraction of features reduces size of the sample set.The genetic algorithm (GA) was used to optimize the parameters of mixed kernel function SVM algorithm.The experimental results show that the proposed method has higher detection probability than MME and CAV algorithms,and has less sensing time than SVM,which has good practicability. |
| format | Article |
| id | doaj-art-bc75c1cea1de4d7ea94e6def4c0fabad |
| institution | Kabale University |
| issn | 1000-0801 |
| language | zho |
| publishDate | 2019-11-01 |
| publisher | Beijing Xintong Media Co., Ltd |
| record_format | Article |
| series | Dianxin kexue |
| spelling | doaj-art-bc75c1cea1de4d7ea94e6def4c0fabad2025-01-15T03:01:59ZzhoBeijing Xintong Media Co., LtdDianxin kexue1000-08012019-11-0135192659585913An efficient spectrum sensing of mixed kernel SVM based on sampling covariance matrixJianyuan NIEJianrong BAOBin JIANGChao LIUFang ZHUJianhai HEIn recent years,with the blind detection algorithms were proposed,more and more blind algorithms based on sampling covariance matrix were applied to spectrum sensing.The detection threshold was an approximation,and the detection performance would be affected for this algorithms.Thus,the mixed kernel function support vector machine (SVM) efficient spectrum sensing based on sampling covariance matrix was proposed.The statistics which were maximum minimum eigenvalue (MME) and covariance absolute value (CAV) of sensing signal sampling covariance matrices were used as the feature vectors of SVM and were trained to generate a spectrum sensing classifier.The advantage of this algorithm was that it needn’t calculate the detection threshold and the extraction of features reduces size of the sample set.The genetic algorithm (GA) was used to optimize the parameters of mixed kernel function SVM algorithm.The experimental results show that the proposed method has higher detection probability than MME and CAV algorithms,and has less sensing time than SVM,which has good practicability.http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2019210/detection thresholdmixed kernel functionSVMMMEGA |
| spellingShingle | Jianyuan NIE Jianrong BAO Bin JIANG Chao LIU Fang ZHU Jianhai HE An efficient spectrum sensing of mixed kernel SVM based on sampling covariance matrix Dianxin kexue detection threshold mixed kernel function SVM MME GA |
| title | An efficient spectrum sensing of mixed kernel SVM based on sampling covariance matrix |
| title_full | An efficient spectrum sensing of mixed kernel SVM based on sampling covariance matrix |
| title_fullStr | An efficient spectrum sensing of mixed kernel SVM based on sampling covariance matrix |
| title_full_unstemmed | An efficient spectrum sensing of mixed kernel SVM based on sampling covariance matrix |
| title_short | An efficient spectrum sensing of mixed kernel SVM based on sampling covariance matrix |
| title_sort | efficient spectrum sensing of mixed kernel svm based on sampling covariance matrix |
| topic | detection threshold mixed kernel function SVM MME GA |
| url | http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2019210/ |
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