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Digital communication signals identification using an efficient recognizer

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Bibliographic Details
Title: Digital communication signals identification using an efficient recognizer
Authors: Ebrahimzadeh, A.1 E_zadeh@nit.ac.ir, Azimi, H. Ham_azimi@yahoo.com, Mirbozorgi, S.A.1 Sa.mirbozorgi@ieee.org
Source: Measurement (02632241). Oct2011, Vol. 44 Issue 8, p1475-1481. 7p.
Subjects: Digital communications, Signals & signaling, Signal-to-noise ratio, Feature extraction, Radial basis functions, Artificial neural networks, Simulation methods & models
Abstract: Abstract: Automatic recognition of the communication signals plays an important role for various applications. Most of the existing techniques require high levels of signal to noise ratio (SNR). In this paper, we propose a high efficient technique for classification of the digital modulations that requires a low level of SNRs. This technique includes two main modules: feature extraction module and the classifier module. In the feature extraction module we use the auto-regressive modeling together other useful features. These features are a combination set of the entropy and energy of the signal, variance of the coefficients wavelet packet transform, fourth order of moment and zero-crossing rate. In the classifier module we have used the two structures of the neural networks: multi-layer perceptron (MLP) neural network and radial basis neural networks. Simulation results show the proposed technique has very high recognition accuracy for identification of the considered digital modulations even at very low SNRs. [Copyright &y& Elsevier]
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Database: Engineering Source
Description
Abstract:Abstract: Automatic recognition of the communication signals plays an important role for various applications. Most of the existing techniques require high levels of signal to noise ratio (SNR). In this paper, we propose a high efficient technique for classification of the digital modulations that requires a low level of SNRs. This technique includes two main modules: feature extraction module and the classifier module. In the feature extraction module we use the auto-regressive modeling together other useful features. These features are a combination set of the entropy and energy of the signal, variance of the coefficients wavelet packet transform, fourth order of moment and zero-crossing rate. In the classifier module we have used the two structures of the neural networks: multi-layer perceptron (MLP) neural network and radial basis neural networks. Simulation results show the proposed technique has very high recognition accuracy for identification of the considered digital modulations even at very low SNRs. [Copyright &y& Elsevier]
ISSN:02632241
DOI:10.1016/j.measurement.2011.05.019