CNN-based automatic modulation recognition for index modulation systems

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dc.authoridLeblebici, Merih/0000-0002-7709-2906en_US
dc.authoridCİCİOĞLU, MURTAZA/0000-0002-5657-7402en_US
dc.authoridÇalhan, Ali/0000-0002-5798-3103en_US
dc.authorscopusid58723537700en_US
dc.authorscopusid16548877100en_US
dc.authorscopusid57203170833en_US
dc.authorwosidLeblebici, Merih/HZK-6228-2023en_US
dc.authorwosidCİCİOĞLU, MURTAZA/AAL-5004-2020en_US
dc.authorwosidÇalhan, Ali/H-1375-2014en_US
dc.contributor.authorLeblebici, Merih
dc.contributor.authorCalhan, Ali
dc.contributor.authorCicioglu, Murtaza
dc.date.accessioned2024-08-23T16:04:46Z
dc.date.available2024-08-23T16:04:46Z
dc.date.issued2024en_US
dc.departmentDüzce Üniversitesien_US
dc.description.abstractAutomatic modulation recognition (AMR) has garnered significant attention in both civilian and military domains, with applications ranging from spectrum sensing and cognitive radio (CR) to the deterrence of adversary communication. Index modulation (IM) represents an innovative digital modulation technique that exploits the indices of parameters of communication systems to transmit extra information bits. This paper aims to examine the performance of a convolutional neural network (CNN)-based AMR across various IM systems, including spatial modulation (SM), quadrature spatial modulation (QSM), and generalized spatial modulation (GSM) with eight digital modulation schemes. In this study, we leverage confusion matrices, receiver operating characteristic (ROC) curves, and F1 scores to illustrate the recognition model's outputs.en_US
dc.description.sponsorshipDuezce University Scientific Research Project [BAP-2023.06.01.1410]en_US
dc.description.sponsorshipThis work was supported by the Duezce University Scientific Research Project under Grant BAP-2023.06.01.1410.en_US
dc.identifier.doi10.1016/j.eswa.2023.122665
dc.identifier.issn0957-4174
dc.identifier.issn1873-6793
dc.identifier.scopus2-s2.0-85177828987en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1016/j.eswa.2023.122665
dc.identifier.urihttps://hdl.handle.net/20.500.12684/14358
dc.identifier.volume240en_US
dc.identifier.wosWOS:001125603800001en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherPergamon-Elsevier Science Ltden_US
dc.relation.ispartofExpert Systems With Applicationsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectAutomatic modulation recognitionen_US
dc.subjectConvolutional neural networken_US
dc.subjectIndex modulationen_US
dc.subjectMachine learningen_US
dc.subjectSpatial Modulationen_US
dc.subjectClassificationen_US
dc.subjectPerformanceen_US
dc.subjectOfdmen_US
dc.titleCNN-based automatic modulation recognition for index modulation systemsen_US
dc.typeArticleen_US

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