Digital modulation classification by support vector machines and Hilbert-Huang transformation

Authors:
Zhijin Zhao;Weiguo Hu;Dianwu Guo
Affiliations:
School of Telecommunication, Hangzhou Dianzi University, Hangzhou, Zhe Jiang, China;School of Telecommunication, Hangzhou Dianzi University, Hangzhou, Zhe Jiang, China;School of Telecommunication, Hangzhou Dianzi University, Hangzhou, Zhe Jiang, China
Venue:
ICCOM'05 Proceedings of the 9th WSEAS International Conference on Communications
Year:
2005

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Abstract

Support Vector Machines (SVMs) map inputs vectors nonlinearly into a high dimensional feature space and construct the optimum separating hyperplane in space to realize signal classification. Automatic classification of digital modulation signals plays an important role in communication applications such as an intelligent demodulator, interference identification and monitoring, so many investigations have been carried out in the past. Hilbert-Huang transformation (HHT) is a novel method of time frequency analysis for nonlinear and non-stationary data. In this paper, a new method based on SVM and HHT for classifying BFSK, BPSK and 16QAM is proposed. The method can classify these signals well, and the correct classification rates are above 88%.