Telecommunications Science ›› 2019, Vol. 35 ›› Issue (11): 19-26.doi: 10.11959/j.issn.1000-0801.2019210

• research and development • Previous Articles     Next Articles

An efficient spectrum sensing of mixed kernel SVM based on sampling covariance matrix

Jianyuan NIE1,Jianrong BAO1,Bin JIANG1,Chao LIU1(),Fang ZHU1,Jianhai HE2   

  1. 1 School of Communication Engineering,Hangzhou Dianzi University,Hangzhou 310018,China
    2 School of Electronic Information Engineering,Ningbo Polytechnic,Ningbo 315800,China
  • Revised:2019-08-19 Online:2019-11-01 Published:2019-12-23
  • Supported by:
    Natural Science Foundation of Zhejiang Province(LY17F010019);The National Natural Science Foundation of China(U1809201);Public Welfare Technology Application Research Program of Zhejiang Province(LGG18F010011);Public Welfare Technology Application Research Program of Zhejiang Province(LGG19F010004)


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.

Key words: detection threshold, mixed kernel function, SVM, MME, GA

CLC Number: 

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