Deep long short-term memory networks-based automatic recognition of six different digital modulation types under varying noise conditions

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dc.authorid0000-0003-1840-9958en_US
dc.authorid0000-0001-7345-2727
dc.authorid0000-0001-5375-3012
dc.contributor.authorDaldal, Nihat
dc.contributor.authorYıldırım, Özal
dc.contributor.authorPolat, Kemal
dc.date.accessioned2021-06-23T19:51:36Z
dc.date.available2021-06-23T19:51:36Z
dc.date.issued2019
dc.departmentBAİBÜ, Mühendislik Fakültesi, Elektrik Elektronik Mühendisliği Bölümüen_US
dc.description.abstractIn this paper, a new method based on deep learning has been proposed in order to recognize noise-digital modulation signals at varying noise levels automatically. The 8-bit data from six different modulations have been obtained by adding noise levels from 5 to 25dB. The used digital modulation types are Amplitude Shift Keying, Frequency Shift Keying, Phase Shift Keying, Quadrature Amplitude Shift Keying, Quadrature Frequency Shift Keying, and Quadrature Phase Shift Keying. To recognize the noise-digital modulation signals automatically, a new deep long short-term memory networks (LSTMs) model has been proposed and then applied to these signals successfully. A significant advantage of the proposed system is that deep learning method has been trained and tested with raw digital modulation signals without applying any feature extraction from the signals. In this study, the noise modulation signals of 5-25dB have been classified and compared with each other. The innovative aspect of the study is to classify the modulation with the LSTM method without dealing with the extraction of signal characteristics. Without noise, added digital modulation signals had been classified as the success rate of 97.22%, while with all noise-added signals have been classified as the success rate of 94.72% with deep LSTM model. The experimental results show that the proposed deep LSTM model has been achieved remarkable results in recognition of noised six different modulation signals with a fully end-to-end structure.en_US
dc.identifier.doi10.1007/s00521-019-04261-2
dc.identifier.endpage1981en_US
dc.identifier.issn0941-0643
dc.identifier.issn1433-3058
dc.identifier.issue6en_US
dc.identifier.scopus2-s2.0-85183475385en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage1967en_US
dc.identifier.urihttps://doi.org/10.1007/s00521-019-04261-2
dc.identifier.urihttps://hdl.handle.net/20.500.12491/10014
dc.identifier.volume31en_US
dc.identifier.wosWOS:000470746700022en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorDaldal, Nihat
dc.institutionauthorPolat, Kemal
dc.language.isoenen_US
dc.publisherSpringer London Ltden_US
dc.relation.ispartofNeural Computing & Applicationsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectModulation-type Classificationen_US
dc.subjectDigital Modulationen_US
dc.subjectDeep Learningen_US
dc.subjectLSTM Networken_US
dc.titleDeep long short-term memory networks-based automatic recognition of six different digital modulation types under varying noise conditionsen_US
dc.typeArticleen_US

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