Automatic determination of digital modulation types with different noises using convolutional neural network based on time-frequency information

Basit Öğe Kaydı
dc.authorid0000-0003-1840-9958en_US
dc.authorid0000-0001-5256-7648en_US
dc.authorid0000-0001-7345-2727
dc.contributor.authorDaldal, Nihat
dc.contributor.authorCömert, Zafer
dc.contributor.authorPolat, Kemal
dc.date.accessioned2021-06-23T19:54:57Z
dc.date.available2021-06-23T19:54:57Z
dc.date.issued2020
dc.departmentBAİBÜ, Mühendislik Fakültesi, Elektrik Elektronik Mühendisliği Bölümüen_US
dc.description.abstractIn this study, a novel digital modulation classification model has been proposed for automatically recognizing six different modulation types including amplitude shift keying (ASK), frequency shift keying (FSK), phase-shift keying (PSK), quadrate amplitude shift keying (QASK), quadrate frequency shift keying (QFSK), and quadrate phase-shift keying (QPSK). The determination of modulation type is significant in military communication, satellite communication systems, and submarine communication. To classify the modulation types, we have proposed a two-stage hybrid method combining short-time Fourier transform (STFT) and convolutional neural network (CNN). In the first stage, as the data source, the time-frequency information from these modulation signals have been extracted with STFT. This information has been obtained as 2D images to feed the input of the CNN deep learning method. In the second stage, the obtained 2D time-frequency information has been given to the input of the CNN algorithm to classify the modulation types. In this work, noises at various SNR values from 0 dB to 25 dB were created and added to the modulated signals. Even in the presence of noise, the proposed hybrid deep learning model achieved excellent results in the noised-modulation signals. (C) 2019 Elsevier B.V. All rights reserved.en_US
dc.identifier.doi10.1016/j.asoc.2019.105834
dc.identifier.issn1568-4946
dc.identifier.issn1872-9681
dc.identifier.scopus2-s2.0-85073822518en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1016/j.asoc.2019.105834
dc.identifier.urihttps://hdl.handle.net/20.500.12491/10689
dc.identifier.volume86en_US
dc.identifier.wosWOS:000503388200027en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorDaldal, Nihat
dc.institutionauthorPolat, Kemal
dc.institutionauthorPolat, Kemal
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.relation.ispartofApplied Soft Computingen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectModulation Type Classificationen_US
dc.subjectDigital Modulationen_US
dc.subjectDeep Learningen_US
dc.subjectConvolutional Neural Network (CNN)en_US
dc.subjectShort-time Fourier Transform (STFT)en_US
dc.titleAutomatic determination of digital modulation types with different noises using convolutional neural network based on time-frequency informationen_US
dc.typeArticleen_US

Dosyalar

Orijinal paket
Listeleniyor 1 - 1 / 1
Küçük Resim Yok
İsim:
nihat-daldal.pdf
Boyut:
3.29 MB
Biçim:
Adobe Portable Document Format
Açıklama:
Tam Metin/Full Text
İndir

Koleksiyon

WoS İndeksli Yayınlar Koleksiyonu
Elektrik Elektronik Mühendisliği Bölümü Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu