Deep learning-based modulation recognition with constellation diagram: A case study

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dc.authoridCİCİOĞLU, MURTAZA/0000-0002-5657-7402en_US
dc.authoridLeblebici, Merih/0000-0002-7709-2906en_US
dc.authoridÇalhan, Ali/0000-0002-5798-3103en_US
dc.authorscopusid58723537700en_US
dc.authorscopusid16548877100en_US
dc.authorscopusid57203170833en_US
dc.authorwosidCİCİOĞLU, MURTAZA/AAL-5004-2020en_US
dc.authorwosidLeblebici, Merih/HZK-6228-2023en_US
dc.authorwosidÇalhan, Ali/H-1375-2014en_US
dc.contributor.authorLeblebici, Merih
dc.contributor.authorCalhan, Ali
dc.contributor.authorCicioglu, Murtaza
dc.date.accessioned2024-08-23T16:04:35Z
dc.date.available2024-08-23T16:04:35Z
dc.date.issued2024en_US
dc.departmentDüzce Üniversitesien_US
dc.description.abstractAutomatic modulation recognition is a promising solution for identifying and classifying signals received in heterogeneous wireless networks. In dynamic and autonomous environments, receivers must extract the relevant signal from various modulated signals to enable further communication procedures. Machine learning, including its sub-branches for classification problems, offers promising operational capabilities. This study utilized the ResNet-50 deep learning method for modulation classification. A dataset consisting of eight digital modulation techniques was generated, with constellation diagrams created as image data over the additive white Gaussian noise (AWGN) channel at signal-to-noise ratios (SNR) of 5 dB, 10 dB, and 20 dB. The deep learning algorithm's performance metrics were evaluated using a confusion matrix, and F1 scores were compared to those of the AlexNet deep learning algorithm. The simulation results clearly indicate the superior performance of ResNet-50 over AlexNet. In terms of average F1 scores, ResNet-50 exhibits a significant advantage, surpassing AlexNet by approximately 67%, 29%, and 10% at SNR values of 5 dB, 10 dB, and 20 dB, respectively.en_US
dc.description.sponsorshipDuzce University Scientific Research Project [BAP -2023.06.01.1410]en_US
dc.description.sponsorshipThis work was supported by the Duzce University Scientific Research Project under Grant BAP -2023.06.01.1410.en_US
dc.identifier.doi10.1016/j.phycom.2024.102285
dc.identifier.issn1874-4907
dc.identifier.scopus2-s2.0-85182504603en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.urihttps://doi.org/10.1016/j.phycom.2024.102285
dc.identifier.urihttps://hdl.handle.net/20.500.12684/14279
dc.identifier.volume63en_US
dc.identifier.wosWOS:001165166000001en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.relation.ispartofPhysical Communicationen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectModulation recognitionen_US
dc.subjectDeep learningen_US
dc.subjectConstellation diagramen_US
dc.subjectResNet-50en_US
dc.subjectClassificationen_US
dc.subjectNetworksen_US
dc.titleDeep learning-based modulation recognition with constellation diagram: A case studyen_US
dc.typeArticleen_US

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