DocumentCode :
3432396
Title :
Modulation Classification Based on Spectral Correlation and SVM
Author :
Xiaoyun Teng ; Pengwu Tian ; Hongyi Yu
Author_Institution :
Dept. of Commun. Eng., Zhengzhou Inf. Sci. & Technol. Inst., Zhengzhou
fYear :
2008
fDate :
12-14 Oct. 2008
Firstpage :
1
Lastpage :
4
Abstract :
This paper addresses the problem of automatic modulation recognition of digital signals. A classification method based on spectral correlation and Support Vector Machine (SVM) is developed. The spectral correlation theory is introduced and several characteristic parameters which can be used for modulation analysis are extracted. The parameters are used as the input feature vectors to SVM. SVM maps the vectors into a high dimensional feature space, so the problem of non-separable classification in low dimension is resolved and the decision threshold become unnecessary. The experiment results show that the algorithm is robust with high accuracy even at low SNR.
Keywords :
correlation theory; feature extraction; modulation; pattern classification; signal processing; support vector machines; SVM; automatic modulation recognition; digital signals; modulation classification; spectral correlation; support vector machine; Digital modulation; Feature extraction; Frequency; Information science; Pattern recognition; Pulse modulation; Signal processing; Signal processing algorithms; Support vector machine classification; Support vector machines;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Wireless Communications, Networking and Mobile Computing, 2008. WiCOM '08. 4th International Conference on
Conference_Location :
Dalian
Print_ISBN :
978-1-4244-2107-7
Electronic_ISBN :
978-1-4244-2108-4
Type :
conf
DOI :
10.1109/WiCom.2008.409
Filename :
4678318
Link To Document :
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