Digital Modulation Classification Using Wavelet Transform and Artificial Neural Network

Volume 13 , Issue 1 , January 2010 , Pages 59-70

Authors

¹ College of Engineering, Salahaddin University

Received signals contain a vast amount of uncertainty due to the unknown modulating signals,

communication channel, and noise. Therefore the modulation classification problem has to be

approached based on artificial neural networks . In this work a digital modulation classification method

is presented, based on discrete wavelet transform (DWT) and artificial neural networks (ANN) to

distinguish digital modulation, like quadrature amplitude (QAM), phase shift keying (PSK), and

frequency shift keying (FSK) signals. Feature extraction is performed via the DWT detail coefficients of

the digital signals using (db4) mother wavelet, because of the usefulness of wavelet in signal de-noising

.The extracted features are presented to an ANN for pattern recognition. In this work Levenberg-

Marquardt error back propagation algorithm is used since it appears to be the fastest method for

training moderate-sized feed forward neural networks (up to several hundred weights).The performance

of the classification scheme is investigated through simulations using matlab-7, high recognition rates

are obtained of about (97%).However, there are probabilities of misclassification of about (3%).

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