PAM Signals Classification Using Modified Gabor Filter NetworkReport as inadecuate




PAM Signals Classification Using Modified Gabor Filter Network - Download this document for free, or read online. Document in PDF available to download.

Mathematical Problems in Engineering - Volume 2015 2015, Article ID 262180, 10 pages -

Research Article

ISRA University, Islamabad, Pakistan

Department of Electronics Engineering, International Islamic University, Islamabad 44000, Pakistan

AIR University, Islamabad, Pakistan

Received 9 September 2014; Revised 2 April 2015; Accepted 15 April 2015

Academic Editor: Angel Sánchez

Copyright © 2015 Sajjad Ahmed Ghauri and Ijaz Mansoor Qureshi. 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.

Abstract

A Modified Gabor Filter MGF network based approach is used for feature extraction and classification of -ary Pulse Amplitude Modulated -PAM signals by adaptively tuning the parameters of MGF network. Modulation classification of -PAM signals is done under the influence of additive white Gaussian noise AWGN and channel effects such as Rayleigh flat fading and Rician flat fading. The MGF network uses the network structure of two layers. First layer which is input layer constitutes the adaptive feature extraction part and second layer constitutes the signal classification part. The Gabor atom parameters are tuned using Delta rule and updating of weights of MGF using Recursive Least Square RLS algorithm. The simulation results in the form confusion matrix show that proposed modified modulation classification algorithm has high classification accuracy at low signal to noise ratio SNR. The performance comparison with state-of-the-art existing techniques shows the significant performance improvement of proposed MGF based classifier.





Author: Sajjad Ahmed Ghauri and Ijaz Mansoor Qureshi

Source: https://www.hindawi.com/



DOWNLOAD PDF




Related documents