Digital Signals Classification in Cognitive Radio Based on Discrete Wavelet Transform

Author

Kannan, Ravindran ; Ravi, Siddarth

Author_Institution

Dept. of ECE, Sathyabama Univ., Chennai, India

fYear

2012

fDate

7-9 Dec. 2012

Firstpage

870

Lastpage

873

Abstract

In this paper, Discrete Wavelet Transform (DWT) based digital signals classification is proposed. First, the modulated signals are decomposed by using DWT. Secondly, a set of biggest wavelet coefficients is selected for training the classifier. Thirdly, a supervised classifier system based on SVM is constructed to classify the modulation scheme of the unknown signal. The modulation schemes used in the proposed systems are DPSK, PSK and MSK. The modulated signals are passed through an Additive White Gaussian Noise (AWGN) channel before feature extraction. 400 generated signals are used to evaluate the proposed system. The maximum classification rate achieved by the proposed system is 75% to 97% while using 30% biggest wavelet coefficients.

Keywords

AWGN channels; cognitive radio; feature extraction; minimum shift keying; phase shift keying; signal classification; support vector machines; telecommunication computing; wavelet transforms; AWGN channel; DPSK; DWT; MSK; PSK; SVM; additive white Gaussian noise channel; cognitive radio; differential phase shift keying; digital signal classification; discrete wavelet transform; feature extraction; minimum shift keying; modulation scheme; wavelet coefficients; Accuracy; Discrete wavelet transforms; Modulation; Signal to noise ratio; Support vector machines; Training; Wavelet coefficients; discrete wavelet transform; modulation schemes; software defined radio; support vector machine;

fLanguage

English

Publisher

ieee

Conference_Titel

Control Engineering and Communication Technology (ICCECT), 2012 International Conference on

Conference_Location

Liaoning

Print_ISBN

978-1-4673-4499-9

Type

conf

DOI

10.1109/ICCECT.2012.156

Filename

6413975