基于随机共振和支持向量机的频谱感知  

Spectrum sensing method using SR and SVM

作　　者：叶翔 聂勇[1] 翟旭平[1] 

机构地区：[1]上海大学通信与信息工程学院特种光纤与光接入网重点实验室,上海200072

出　　处：《电子测量技术》2017年第12期121-125,共5页Electronic Measurement Technology

基　　金：国家自然科学基金(61171085)资助项目

摘　　要：认知无线电是一门新技术,它的目标实现用户的动态频谱接入,提高频谱的使用效率。频谱感知是认知无线电的基础,它的作用是感知频谱空洞,实现对主用户无干扰接入。频谱感知需要在极低信噪比下有较好的感知性能,才能保证感知用户在不影响主用户通信的情况下进行通信。即使在较低的信噪比条件下,它也需要较高的检测性能。提出一种对信号进行双稳态随机共振和特征识别的方法,首先信号先通过随机共振系统以提高信噪比,然后使用FAM算法将信号的循环谱特征提取出来。最后使用支持向量机(SVM)对特征进行模式识别。实验结果表明,在低信噪比下本方法相比传统的能量检测和SVM方法具有更高的检测可靠性。Cognitive radio is a new technology,which aims to achieve the user＇s dynamic spectrum access and improve the efficiency of the spectrum.Spectrum sensing is the basic of cognitive radio,and spectrum sensing is used to discover the spectral holes in cognitive radio networks,which allows secondary users（SU）to communicate without causing harmful interference to primary users（PU）.It requires good perceiving performance even at very low signal-tonoise ratios to ensure that the perceived user communicates without affecting the primary user′s communication in spectrum sensing.This paper presents an efficient sensing algorithm based on stochastic resonance（SR）and support vector machine（SVM）.Firstly,the detecting signal through the Stochastic resonance system to improve signal-to-noise rate and strength the feature of signal.Secondly the FAM algorithm is used to extract the cyclostationary feature of signal.Finally,we use support vector machine to classify the feature to get the detection results.The experimental results show that the method proposed in this paper has higher detection reliability than the traditional detection method at low SNR.

关 键 词：频谱感知 随机共振 支持向量机 循环平稳特征 

分 类 号：TN929.5[电子电信—通信与信息系统]