Recognition of communication signal types using genetic algorithm and support vector machines based on the higher order statistics

Authors:
Ataollah Ebrahimzadeh Shermeh;Reza Ghazalian
Affiliations:
Faculty of Electrical and Computer Engineering, Babol University of Technology, Babol, Iran;Faculty of Electrical and Computer Engineering, Babol University of Technology, Babol, Iran
Venue:
Digital Signal Processing
Year:
2010

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Abstract

Automatic recognition of communication signal type plays an important role in various applications. Most of the existing recognizers can only identify a few types of communication signal. This paper presents a novel intelligent technique that identifies a variety of digital signal types. Here, a hierarchical support vector machine based structure is proposed as the multiclass classifier. A proper set of the higher order moments (up to eighth) and higher order cumulants (up to eighth) are proposed as the effective features for recognizing of the digital communication signal. A genetic algorithm is used for selecting the suitable parameters of support vector machines. This idea improves the performance of the recognizer, efficiently. Simulation results show that the proposed recognizer has a high success rate for recognition of the different modulations even at very low SNRs.