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Recognition of the Communication Signals Using Particle Swarm Optimization and Support Vector Machine Based on the Multi-Resolution Wavelet Analysis

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Abstract

Automatic recognition of the communication signals plays an important role for various applications. This paper presents a novel intelligent system for recognition of digital communication signals. This system includes three main modules: feature extraction module, classifier module and optimization module. In the feature extraction module, multi-resolution wavelet analysis is proposed for extraction the suitable features. In the classifier module, a multi-class support vector machine (SVM) based classifier is proposed as the multi-class classifier. For optimization module, a particle swarm optimization algorithm is proposed to improve the generalization performance of the recognizer. In this module, it is optimized the SVM classifier design by searching for the best value of the parameters that tune its discriminant function, and upstream by looking for the best subset of features that feed the classifier. Simulation results show that the proposed hybrid intelligent system has high performance even at very low signal to noise ratios (SNRs).

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Authors and Affiliations

  1. Faculty of Electrical and Computer Engineering, Babol University of Technology, Babol, Iran

    Ataollah Ebrahimzadeh Shrime & Mahdi Yousefi

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  1. Ataollah Ebrahimzadeh Shrime
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  2. Mahdi Yousefi
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Correspondence to Ataollah Ebrahimzadeh Shrime.

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Shrime, A.E., Yousefi, M. Recognition of the Communication Signals Using Particle Swarm Optimization and Support Vector Machine Based on the Multi-Resolution Wavelet Analysis. Wireless Pers Commun 63, 847–860 (2012). https://doi.org/10.1007/s11277-010-0170-x

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  • DOI: https://doi.org/10.1007/s11277-010-0170-x

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