Modulation Identification for OFDM in Multipath Circumstance Based on Support Vector Machine

被引：5
|
作者：
Wang, Hongwei [1 ]
Li, Bingbing [1 ]
Wang, Yongjuan [1 ]
机构：
[1] Xidian Univ, ISN, Natl Key Lab, Xian 710071, Shaanxi, Peoples R China
来源：
2008 11TH IEEE SINGAPORE INTERNATIONAL CONFERENCE ON COMMUNICATION SYSTEMS (ICCS), VOLS 1-3 | 2008年
关键词：
recognition; identification; cumulants; wavelets; support vector machine(SVM); generalize;
D O I：
10.1109/ICCS.2008.4737403
中图分类号：
TP3 [计算技术、计算机技术];
学科分类号：
0812 ;
摘要：
This paper discusses the modulation recognition for OFDM signals in lower Signal Noise Ratio (SNR) and multipath circumstance. As the studies on signal recognition seldom take the multipath circumstance into account, and the traditional recognition method either have the disadvantages in the case of limited train samples or have the low recognition rate in lower SNR, a new method of modulation identification for OFDM signals in multipath circumstance based on support vector machine (SVM) is presented. To an SVM classifier, how to select the feature parameters to construct a feature vector makes a great impact on its performance. In this paper, the cumulants and the wavelets used as classification feature vectors are investigated. Simulation results indicate that the proposed feature vectors have good performances in lower SNR and multipath circumstance, and the SVM classifier's generalizing ability proves to be good.
引用
收藏
页码：1349 / 1353
页数：5
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