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

Jianyuan NIE; Jianrong BAO; Bin JIANG; Chao LIU; Fang ZHU; Jianhai HE

doi:10.11959/j.issn.1000-0801.2019210

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

research and development | 更新时间：2024-06-05

- An efficient spectrum sensing of mixed kernel SVM based on sampling covariance matrix
- Telecommunications Science Vol. 35, Issue 11, Pages: 19-26(2019)
- 作者机构：
  
  1. 杭州电子科技大学通信工程学院，浙江杭州 310018
  2. 宁波职业技术学院电子信息工程学院，浙江宁波 315800
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.1000-0801.2019210
  CLC： TN925
- Published Online：2019-11，
  
  Published：20 November 2019
- 稿件说明：
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Jianyuan NIE, Jianrong BAO, Bin JIANG, et al. An efficient spectrum sensing of mixed kernel SVM based on sampling covariance matrix[J]. Telecommunications science, 2019, 35(11): 19-26.
DOI：

Jianyuan NIE, Jianrong BAO, Bin JIANG, et al. An efficient spectrum sensing of mixed kernel SVM based on sampling covariance matrix[J]. Telecommunications science, 2019, 35(11): 19-26. DOI： 10.11959/j.issn.1000-0801.2019210.

摘要

近年来随着盲检测算法的提出，越来越多的基于采样协方差矩阵的盲检测算法应用于频谱感知。针对其检测门限是近似值，检测性能会受到影响等问题，提出了基于采样协方差矩阵的混合核函数的支持向量机（support vector machine

SVM）高效频谱感知，通过感知信号采样协方差矩阵的最大最小特征值（maximum minimum eigenvalue

MME）和协方差绝对值（covariance absolute value

CAV）提取的统计量作为SVM的特征向量并训练其生成频谱感知的分类器，无需计算检测门限并且特征提取减少了样本集的大小。利用遗传算法（genetic algorithm

GA）优化混合核函数的SVM的参数。实验结果表明，该方法比MME算法和CAV算法的检测概率有所提高，并且比SVM减少了感知时间，具有良好的实用性。

Abstract

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.

关键词

Keywords

references

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