tailieunhanh - Báo cáo sinh học: " Research Article Automatic Modulation Recognition Using Wavelet Transform and Neural Networks in Wireless Systems"

Tuyển tập các báo cáo nghiên cứu về sinh học được đăng trên tạp chí sinh học Journal of Biology đề tài:Research Article Automatic Modulation Recognition Using Wavelet Transform and Neural Networks in Wireless Systems | Hindawi Publishing Corporation EURASIP Journal on Advances in Signal Processing Volume 2010 Article ID 532898 13 pages doi 2010 532898 Research Article Automatic Modulation Recognition Using Wavelet Transform and Neural Networks in Wireless Systems K. Hassan 11. Dayoub 2 W. Hamouda 3 and M. Berbineau1 1 Universite Lille Nord de France F-59000 Lille INRETS LEOST F-59650 Villeneuve d Ascq France 2 Universite Lille Nord de France F-59000 Lille IEMN DOAE F-59313 Valenciennes France 3 Concordia University Montreal QC Canada H3G 1M8 Correspondence should be addressed to W. Hamouda hamouda@ Received 24 December 2009 Revised 25 June 2010 Accepted 28 June 2010 Academic Editor Azzedine Zerguine Copyright 2010 K. Hassan 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. Modulation type is one of the most important characteristics used in signal waveform identification. In this paper an algorithm for automatic digital modulation recognition is proposed. The proposed algorithm is verified using higher-order statistical moments HOM of continuous wavelet transform CWT as a features set. A multilayer feed-forward neural network trained with resilient backpropagation learning algorithm is proposed as a classifier. The purpose is to discriminate among different M-ary shift keying modulation schemes and the modulation order without any priori signal information. Pre-processing and features subset selection using principal component analysis is used to reduce the network complexity and to improve the classifier s performance. The proposed algorithm is evaluated through confusion matrix and false recognition probability. The proposed classifier is shown to be capable of recognizing the modulation scheme with high accuracy over wide signal-to-noise ratio SNR range over both additive white Gaussian .