Classification of multi-carrier digital modulation signals using NCM clustering based feature-weighting method

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dc.authorid0000-0003-1814-9682en_US
dc.authorid0000-0003-1840-9958en_US
dc.authorid0000-0001-7345-2727
dc.contributor.authorDaldal, Nihat
dc.contributor.authorPolat, Kemal
dc.contributor.authorGuo, Yanhui
dc.date.accessioned2021-06-23T19:51:17Z
dc.date.available2021-06-23T19:51:17Z
dc.date.issued2019
dc.departmentBAİBÜ, Mühendislik Fakültesi, Elektrik Elektronik Mühendisliği Bölümüen_US
dc.description.abstractThis work presents a novel digital modulation signal classification model by combining Neutrosophic c-means (NCM) based feature weighting (NCMBFW) and classifier algorithms. As the digital modulation signal, the multi-carrier amplitude shift keying (MC-ASK), frequency shift keying (MC-FSK), and phase shift keying (MC-PSK) modulation types are employed. In the first step, the feature extraction process has been conducted from the raw digital modulation signals and thereby extracted time, frequency, and timefrequency domain features from the multi-carrier ASK, FSK, and PSK signals. After that, these features have been weighted by using NCMBFW. Finally, classifier algorithms including Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), k-nearest neighbor (k-NN), AdaBoostM1, and Random Forest, have been used to determine the types of digital modulation signals automatically. Many metrics are used to evaluate the performance in the experiments. The proposed method in the classification of MC digital modulation signals is the first work with respect to the classification of MC modulation signals in the literature. For worst case (in 5 dB), while the obtained f-measure values are 0.842, 0.848, 0.863, 0.842, and 0.894 using LDA, SVM, k-NN, AdaBoostM1, and Random Forest classifiers without NCMBFW, respectively, while the f-measure values by combining NCMBFW with classifier algorithms are 0.983, 0.976, 0.992, 0.988, and 0.991, respectively. The experimental results show that the proposed NCMBFW can be considered as a promising tool to improve the classification performance of digital multi-carrier modulation signals. (C) 2019 Elsevier B.V. All rights reserved.en_US
dc.identifier.doi10.1016/j.compind.2019.04.005
dc.identifier.endpage58en_US
dc.identifier.issn0166-3615
dc.identifier.issn1872-6194
dc.identifier.scopus2-s2.0-85064932055en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage45en_US
dc.identifier.urihttps://doi.org/10.1016/j.compind.2019.04.005
dc.identifier.urihttps://hdl.handle.net/20.500.12491/9957
dc.identifier.volume109en_US
dc.identifier.wosWOS:000470943400004en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorDaldal, Nihat
dc.institutionauthorPolat, Kemal
dc.language.isoenen_US
dc.publisherElsevier Science Bven_US
dc.relation.ispartofComputers In Industryen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectNeutrosophic C-means (NCM) Based Feature Weightingen_US
dc.subjectClassificationen_US
dc.subjectMulti-carrier Digital Modulation Signalsen_US
dc.subjectMC-ASKen_US
dc.subjectMC-FSKen_US
dc.subjectMC-PSKen_US
dc.titleClassification of multi-carrier digital modulation signals using NCM clustering based feature-weighting methoden_US
dc.typeArticleen_US

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