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dc.contributor.authorUys, L.Y.
dc.contributor.authorHelberg, A.S.J.
dc.contributor.authorGouws, M.
dc.contributor.authorStrydom, J.J.
dc.identifier.citationUys, L.Y. et al. 2017. The performance of feature-based classification of digital modulations under varying SNR and fading channel conditions. IEEE Africon 2017 Proceedings, 18-20 Sept. 2017, Cape Town, South Africa. []en_US
dc.identifier.isbn978-1-5386-2775-4 (Online)
dc.identifier.issn2153-0033 (Online)
dc.description.abstractFeature-based classification is a method used for automatic modulation classification of communication signals. This method requires extraction of various features from a signal. One of the approaches for feature extraction is the use of instantaneous amplitude, phase and frequency. The performance of these features under different SNR conditions is well described in literature [1]-[2], but the effect of fading on the extracted features has received little investigation. In this paper we investigate the quality of the features in a static multipath Rayleigh fading channel under varying channel conditions. It was found that most feature values are distinguishable for the different digital signals in a flat fading channel. This work shows the behaviour of these features in a fading channel under deteriorating conditions and suggests ways to reduce the effect of the fading on the classification of the signalsen_US
dc.subjectAutomatic modulation classificationen_US
dc.subjectFeature based classificationen_US
dc.subjectFeature extractionen_US
dc.subjectRayleigh fadingen_US
dc.subjectFlat fadingen_US
dc.titleThe performance of feature-based classification of digital modulations under varying SNR and fading channel conditionsen_US
dc.contributor.researchID12363626 - Helberg, Albertus Stephanus Jacobus

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