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

Authors:
Ataollah Ebrahimzadeh Shrime;Mahdi Yousefi
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:
Wireless Personal Communications: An International Journal
Year:
2012

Citing 7
Cited 0

An improved moment-based algorithm for signal classification

Signal Processing
Support-Vector Networks

Machine Learning
Swarm intelligence

Swarm intelligence
A Tutorial on Support Vector Machines for Pattern Recognition

Data Mining and Knowledge Discovery
Classification of co-channel communication signals using cyclic cumulants

ASILOMAR '95 Proceedings of the 29th Asilomar Conference on Signals, Systems and Computers (2-Volume Set)
Maximum Log-Likelihood Function-Based QAM Signal Classification over Fading Channels

Wireless Personal Communications: An International Journal
A Wavelet Tour of Signal Processing, Third Edition: The Sparse Way

A Wavelet Tour of Signal Processing, Third Edition: The Sparse Way

Quantified Score

Hi-index	0.00

Visualization

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).