DocumentCode :
2529832
Title :
Automatic Modulation Recognition using Support Vector Machine in Software Radio Applications
Author :
Park, Cheol-Sun ; Jang, Won ; Nah, Sun-Phil ; Kim, Dae Young
Author_Institution :
EW Lab., Agency for Defense Dev.
Volume :
1
fYear :
2007
fDate :
12-14 Feb. 2007
Firstpage :
9
Lastpage :
12
Abstract :
Most of the algorithms proposed in the literature deal with the problem of digital modulation classification. This paper discusses the modulation classifiers capable of classifying both analog and digital modulation signals in military and civilian communications applications. A total of 7 statistical signal features are extracted and used to classify 9 modulation signals. In this paper, we investigate the performance of the two types of SVM classifiers and compare the performance of these SVM classifiers with that of decision tree based and minimum distance based classifiers. In numerical simulations, SVM classifiers indicate good performance (i.e. probability of correct classification > 95%) on an AWGN channel, even at signal-to-noise ratios as low as 5 dB.
Keywords :
AWGN channels; decision trees; modulation; signal classification; software radio; statistical analysis; support vector machines; AWGN channel; automatic modulation recognition; civilian communications; decision tree; digital modulation signal classification; military communications; minimum distance based classifier; signal-to-noise ratios; software radio applications; statistical signal features; support vector machine; Application software; Classification tree analysis; Decision trees; Digital modulation; Feature extraction; Military communication; Numerical simulation; Software radio; Support vector machine classification; Support vector machines; Decision Tree; Minimum Distance; Modulation Classification; Support Vector Machine;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Advanced Communication Technology, The 9th International Conference on
Conference_Location :
Gangwon-Do
ISSN :
1738-9445
Print_ISBN :
978-89-5519-131-8
Type :
conf
DOI :
10.1109/ICACT.2007.358249
Filename :
4195072
Link To Document :
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