Hierarchical Classification of Analog and Digital Modulation Schemes Using Higher-Order Statistics and Support Vector Machines

dc.authorid Yalcinkaya, Bengisu/0000-0003-3644-0692
dc.authorscopusid 57736344000
dc.authorscopusid 57219359056
dc.authorscopusid 7102824862
dc.authorscopusid 6506642154
dc.authorwosid Yalcinkaya, Bengisu/ABD-4291-2020
dc.contributor.author Yalcinkaya, Bengisu
dc.contributor.author Coruk, Remziye Busra
dc.contributor.author Kara, Ali
dc.contributor.author Tora, Hakan
dc.contributor.other Electrical-Electronics Engineering
dc.contributor.other Airframe and Powerplant Maintenance
dc.contributor.other Department of Electrical & Electronics Engineering
dc.date.accessioned 2024-09-10T21:32:58Z
dc.date.available 2024-09-10T21:32:58Z
dc.date.issued 2024
dc.department Atılım University en_US
dc.department-temp [Yalcinkaya, Bengisu; Coruk, Remziye Busra; Tora, Hakan] Atilim Univ, Elect & Elect Engn, TR-06830 Ankara, Turkiye; [Kara, Ali] Gazi Univ, Elect & Elect Engn, TR-06570 Ankara, Turkiye en_US
dc.description Yalcinkaya, Bengisu/0000-0003-3644-0692 en_US
dc.description.abstract Automatic modulation classification (AMC) algorithms are crucial for various military and commercial applications. There have been numerous AMC algorithms reported in the literature, most of which focus on synthetic signals with a limited number of modulation types having distinctive constellations. The efficient classification of high-order modulation schemes under real propagation effects using models with low complexity still remains difficult. In this paper, employing quadratic SVM, a feature-based hierarchical classification method is proposed to accurately classify especially higher-order modulation schemes and its performance is investigated using over the air (OTA) collected data. Statistical features, higher-order moments, and higher-order cumulants are utilized as features. Then, the performances of some well-known classifiers are evaluated, and the classifier presenting the best performance is employed in the proposed hierarchical classification model. An OTA dataset containing 17 analog and digital modulation schemes is used to assess the performance of the proposed classification model. With the proposed hierarchical classification algorithm, a significant improvement has been achieved, especially in higher-order modulation schemes. The overall accuracy with the proposed hierarchical structure is 96% after 5 dB signal-to-noise ratio value, approximately a 10% increase is achieved compared to the traditional classification algorithm. en_US
dc.description.woscitationindex Science Citation Index Expanded
dc.identifier.citationcount 0
dc.identifier.doi 10.1007/s11277-024-11285-y
dc.identifier.endpage 847 en_US
dc.identifier.issn 0929-6212
dc.identifier.issn 1572-834X
dc.identifier.issue 2 en_US
dc.identifier.scopus 2-s2.0-85196805371
dc.identifier.scopusquality Q2
dc.identifier.startpage 827 en_US
dc.identifier.uri https://doi.org/10.1007/s11277-024-11285-y
dc.identifier.uri https://hdl.handle.net/20.500.14411/7281
dc.identifier.volume 136 en_US
dc.identifier.wos WOS:001254317200009
dc.identifier.wosquality Q3
dc.institutionauthor Gökdoğan, Bengisu Yalçınkaya
dc.institutionauthor Çoruk, Remziye Büşra
dc.institutionauthor Kara, Ali
dc.institutionauthor Tora, Hakan
dc.language.iso en en_US
dc.publisher Springer en_US
dc.relation.publicationcategory Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı en_US
dc.rights info:eu-repo/semantics/closedAccess en_US
dc.scopus.citedbyCount 3
dc.subject Hierarchical modulation classification en_US
dc.subject Feature extraction en_US
dc.subject Machine learning algorithms en_US
dc.subject Support vector machine en_US
dc.subject Analog modulations en_US
dc.subject Digital modulations en_US
dc.title Hierarchical Classification of Analog and Digital Modulation Schemes Using Higher-Order Statistics and Support Vector Machines en_US
dc.type Article en_US
dc.wos.citedbyCount 3
dspace.entity.type Publication
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