采用对数预处理的SVM频谱感知方法

doi:10.3969/j.issn.0255-8297.2017.06.006

应用科学学报 ›› 2017, Vol. 35 ›› Issue (6): 726-734.doi: 10.3969/j.issn.0255-8297.2017.06.006

采用对数预处理的SVM频谱感知方法

翟旭平, 孟田, 王涛

上海大学特种光纤与光接入网重点实验室, 上海 200072

收稿日期:2016-05-09 修回日期:2016-11-18 出版日期:2017-11-30 发布日期:2017-11-30
作者简介:翟旭平,副教授,研究方向:宽带无线通信、认知无线电、视频无线传输等,E-mail:zhaixp@shu.edu.cn
基金资助:
国家自然科学基金（No.61171085，No.61401266）；上海市教委创新项目基金（No.14ZZ096）资助

SVM Spectrum Sensing Based on Data Pre-processing with Log Function

ZHAI Xu-ping, MENG Tian, WANG Tao

Key Laboratory of Specialty Fiber Optics and Optical Access Networks, Shanghai University, Shanghai 200072, China

Received:2016-05-09 Revised:2016-11-18 Online:2017-11-30 Published:2017-11-30

摘要/Abstract

摘要：

为提高频谱感知灵敏度和准确率，减少训练时间，提出一种采用对数函数预处理的支持向量机频谱感知方法.通过实验平台采集出样本集，在保证频谱感知性能前提下，选取一个尺寸最小且具有适用性的训练样本集，利用对数函数对样本集进行预处理，增大主用户信号存在与不存在时样本的平均值之差.实验表明，经对数函数预处理的样本集送入支持向量机进行训练和测试，其频谱感知性能在低信噪比下有明显提高，检测率达到90%以上.

关键词: 对数函数, 认知无线电, 频谱感知, 支持向量机

Abstract:

To improve probability of detection and reduce training time, this paper proposes a method of support vector machine (SVM) spectrum sensing based on data preprocessing with a log function. A minimum size of training set is selected, which is applicable with good performance in spectrum sensing. The sample sets are generated with laboratory instruments. The obtained sample sets are pre-processed with a log function to increase the mean difference between sample sets with and without primary users (PU). Experimental results show that, after pre-processing, performance of spectrum sensing is significantly improved under low SNR conditions with detection accuracy 90% or better.

Key words: log function, cognitive radio, spectrum sensing, support vector machine (SVM)

中图分类号:

TP181

翟旭平, 孟田, 王涛. 采用对数预处理的SVM频谱感知方法[J]. 应用科学学报, 2017, 35(6): 726-734.

ZHAI Xu-ping, MENG Tian, WANG Tao. SVM Spectrum Sensing Based on Data Pre-processing with Log Function[J]. Journal of Applied Sciences, 2017, 35(6): 726-734.

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采用对数预处理的SVM频谱感知方法

SVM Spectrum Sensing Based on Data Pre-processing with Log Function

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