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
fDate :
4/1/2011 12:00:00 AM
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;
Journal_Title :
Communications Letters, IEEE
DOI :
10.1109/LCOMM.2011.022411.101637
