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The Scientific World Journal
Volume 2014, Article ID 643671, 14 pages
Research Article

A Novel Modulation Classification Approach Using Gabor Filter Network

1ISRA University, Islamabad 44000, Pakistan
2School of Engineering & Applied Sciences (SEAS), ISRA University, Islamabad Campus, I/10 Markaz, Islamabad 44000, Pakistan
3International Islamic University, Islamabad 44000, Pakistan
4AIR University, Islamabad 44000, Pakistan
5Institute of Signals, Systems and Soft Computing (ISSS), Islamabad, Pakistan

Received 24 February 2014; Revised 16 June 2014; Accepted 17 June 2014; Published 14 July 2014

Academic Editor: Nirupam Chakraborti

Copyright © 2014 Sajjad Ahmed Ghauri et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


A Gabor filter network based approach is used for feature extraction and classification of digital modulated signals by adaptively tuning the parameters of Gabor filter network. Modulation classification of digitally modulated signals is done under the influence of additive white Gaussian noise (AWGN). The modulations considered for the classification purpose are PSK 2 to 64, FSK 2 to 64, and QAM 4 to 64. The Gabor filter network uses the network structure of two layers; the first layer which is input layer constitutes the adaptive feature extraction part and the second layer constitutes the signal classification part. The Gabor atom parameters are tuned using Delta rule and updating of weights of Gabor filter using least mean square (LMS) algorithm. The simulation results show that proposed novel modulation classification algorithm has high classification accuracy at low signal to noise ratio (SNR) on AWGN channel.