DocumentCode
1462606
Title
A Front End for Discriminative Learning in Automatic Modulation Classification
Author
Müller, Francisco C B F ; Cardoso, Claudomir, Jr. ; Klautau, Aldebaro
Author_Institution
Signal Process. Lab. (LaPS), Fed. Univ. of Para (UFPA), Belem, Brazil
Volume
15
Issue
4
fYear
2011
fDate
4/1/2011 12:00:00 AM
Firstpage
443
Lastpage
445
Abstract
This work presents a novel method for automatic modulation classification based on discriminative learning. The features are the ordered magnitude and phase of the received symbols at the output of the matched filter. The results using the proposed front end and support vector machines are compared to other techniques. Frequency offset is also considered and the results show that in this condition the new method significantly outperforms two cumulant-based classifiers.
Keywords
learning (artificial intelligence); pattern classification; support vector machines; automatic modulation classification method; cumulant-based classifiers; discriminative learning; frequency offset; front end; matched filter; support vector machines; Accuracy; Modulation; Signal to noise ratio; Support vector machines; Training; Upper bound; Vectors; Modulation classification; likelihood ratio test; support vector machines;
fLanguage
English
Journal_Title
Communications Letters, IEEE
Publisher
ieee
ISSN
1089-7798
Type
jour
DOI
10.1109/LCOMM.2011.022411.101637
Filename
5722074
Link To Document