A new generation communication system based on deep learning methods for the process of modulation and demodulation from the modulated images

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dc.authorid0000-0001-7345-2727en_US
dc.authorid0000-0002-1538-5404en_US
dc.authorid0000-0002-7201-6963en_US
dc.authorid0000-0003-1840-9958en_US
dc.contributor.authorDaldal, Nihat
dc.contributor.authorSezer, Zeynel Abidin
dc.contributor.authorNour, Majid
dc.contributor.authorAlhudhaif, Adi
dc.contributor.authorPolat, Kemal
dc.date.accessioned2024-01-30T12:23:02Z
dc.date.available2024-01-30T12:23:02Z
dc.date.issued2022en_US
dc.departmentBAİBÜ, Mühendislik Fakültesi, Elektrik Elektronik Mühendisliği Bölümüen_US
dc.description.abstractDemodulating the modulated signals used in digital communication on the receiver side is necessary in terms of communication. The currently used systems are systems with a variety of hardware. These systems are used separately for each type of communication signal. A single algorithm facilitates the classification and subsequent demodulation of signals without needing hardware instead of extra hardware cost and complex systems. This study, which aims to make modulation classification by using images of signals, provides this convenience. In this study, a classification and demodulation process is done by using images of digital modulation signals. Convolutional neural network (CNN), a deep learning algorithm, has been used for classification and recognition. Images of the signals of quadrate amplitude shift keying (QASK), quadrate frequency shift keying (QFSK), and quadrate phase shift keying (QPSK) digital modulation types at noise levels of 0 dB, 5 dB, 10 dB, and 15 dB were used. Thanks to this algorithm, which works without hardware, the success achieved is around 98%. Python programming language and libraries have been used in training and testing the algorithm. Demodulation processes of these signals have been performed for demodulation using the nonlinear autoregressive network with exogenous inputs (NARX) algorithm, an artificial neural network. As a result of using MATLAB, the NARX algorithm achieved approximately 94% success in obtaining the information signal. Thanks to the work done, it will be possible to classify and demodulate other communication signals without extra hardware.en_US
dc.identifier.citationDaldal, N., Sezer, Z. A., Nour, M., Alhudhaif, A., & Polat, K. (2022). A New Generation Communication System Based on Deep Learning Methods for the Process of Modulation and Demodulation from the Modulated Images. Mathematical Problems in Engineering, 2022.en_US
dc.identifier.doi10.1155/2022/9555598
dc.identifier.endpage13en_US
dc.identifier.issn1024-123X
dc.identifier.issn1563-5147
dc.identifier.scopus2-s2.0-85131461151en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.startpage1en_US
dc.identifier.urihttp://dx.doi.org/10.1155/2022/9555598
dc.identifier.urihttps://hdl.handle.net/20.500.12491/11972
dc.identifier.volume2022en_US
dc.identifier.wosWOS:000807819100012en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorDaldal, Nihat
dc.institutionauthorPolat, Kemal
dc.language.isoenen_US
dc.publisherHindawi Ltden_US
dc.relation.ispartofMathematical Problems in Engineeringen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectConvolutional Neural-Networksen_US
dc.subjectClassificationen_US
dc.subjectRecognitionen_US
dc.subjectDropouten_US
dc.titleA new generation communication system based on deep learning methods for the process of modulation and demodulation from the modulated imagesen_US
dc.typeArticleen_US

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