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Communication Dans Un Congrès Année : 2009

Automatic modulation recognition using wavelet transform and neural network

Résumé

Modulation type is one of the most important characteristics used in signal waveform identification. An algorithm for automatic modulation recognition has been developed and presented in this study. The suggested algorithm is verified using higher order statistical moments of wavelet transform as a features set. A multi-layer neural network with resilient backpropagation learning algorithm is proposed as a classifier. The purpose is to discriminate different M-ary shift keying modulation types and modulation order without any priori signal information. Pre-processing and features subset selection using principal component analysis will reduce the network complexity and increase the recognizer performance.
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Dates et versions

hal-00474120 , version 1 (19-04-2010)

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Kais Hassan, Iyad Dayoub, Walaa Hamouda, Marion Berbineau. Automatic modulation recognition using wavelet transform and neural network. 9th International Conference on Intelligent Transport Systems Telecommunications, ITST 2009, Oct 2009, Lille, France. pp.234-238, ⟨10.1109/ITST.2009.5399351⟩. ⟨hal-00474120⟩
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