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Digital communication signals identification using an efficient recognizer

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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]
Copyright of Measurement (02632241) is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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PubType: Academic Journal
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  Data: <searchLink fieldCode="JN" term="%22Measurement+%2802632241%29%22">Measurement (02632241)</searchLink>. Oct2011, Vol. 44 Issue 8, p1475-1481. 7p.
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  Data: <searchLink fieldCode="DE" term="%22Digital+communications%22">Digital communications</searchLink><br /><searchLink fieldCode="DE" term="%22Signals+%26+signaling%22">Signals & signaling</searchLink><br /><searchLink fieldCode="DE" term="%22Signal-to-noise+ratio%22">Signal-to-noise ratio</searchLink><br /><searchLink fieldCode="DE" term="%22Feature+extraction%22">Feature extraction</searchLink><br /><searchLink fieldCode="DE" term="%22Radial+basis+functions%22">Radial basis functions</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Simulation+methods+%26+models%22">Simulation methods & models</searchLink>
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  Data: 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]
– Name: AbstractSuppliedCopyright
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  Data: <i>Copyright of Measurement (02632241) is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1016/j.measurement.2011.05.019
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        Text: English
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      – SubjectFull: Signals & signaling
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      – SubjectFull: Signal-to-noise ratio
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      – SubjectFull: Feature extraction
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      – SubjectFull: Radial basis functions
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      – SubjectFull: Artificial neural networks
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      – SubjectFull: Simulation methods & models
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      – TitleFull: Digital communication signals identification using an efficient recognizer
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              Text: Oct2011
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