3D convolutional neural networks based automatic modulation classification in the presence of channel noise

Basit Öğe Kaydı
dc.authoridNoor, Alam/0000-0002-0077-6509
dc.authoridRehman, Abdul/0000-0002-9343-7652
dc.authoridCengiz, Korhan/0000-0001-6594-8861
dc.authoridUllah, Inam/0000-0002-5879-569X
dc.authoridKhan, Rahim/0000-0002-0928-915X
dc.authorwosidNoor, Alam/B-2353-2019
dc.authorwosidyang, qiang/GYJ-0971-2022
dc.authorwosidREHMAN, ATEEQ UR/ABI-7516-2020
dc.authorwosidRehman, Abdul/D-5630-2019
dc.authorwosidCengiz, Korhan/HTN-8060-2023
dc.authorwosidUllah, Inam/Z-3617-2019
dc.contributor.authorKhan, Rahim
dc.contributor.authorYang, Qiang
dc.contributor.authorUllah, Inam
dc.contributor.authorRehman, Ateeq Ur
dc.contributor.authorBin Tufail, Ahsan
dc.contributor.authorNoor, Alam
dc.contributor.authorRehman, Abdul
dc.date.accessioned2024-06-12T11:20:41Z
dc.date.available2024-06-12T11:20:41Z
dc.date.issued2022
dc.departmentTrakya Üniversitesien_US
dc.description.abstractAutomatic modulation classification is a task that is essentially required in many intelligent communication systems such as fibre-optic, next-generation 5G or 6G systems, cognitive radio as well as multimedia internet-of-things networks etc. Deep learning (DL) is a representation learning method that takes raw data and finds representations for different tasks such as classification and detection. DL techniques like Convolutional Neural Networks (CNNs) have a strong potential to process and analyse large chunks of data. In this work, we considered the problem of multiclass (eight classes) classification of modulated signals, which are, Binary Phase Shift Keying, Quadrature Phase Shift Keying, 16 and 64 Quadrature Amplitude Modulation corrupted by Additive White Gaussian Noise, Rician and Rayleigh fading channels using 3D-CNN architectures in both frequency and spatial domains while deploying three approaches for data augmentation, which are, random zoomed in/out, random shift and random weak Gaussian blurring augmentation techniques with a cross-validation (CV) based hyperparameter selection statistical approach. Simulation results testify the performance of 10-fold CV without augmentation in the spatial domain to be the best while the worst performing method happens to be 10-fold CV without augmentation in the frequency domain and we found learning in the spatial domain to be better than learning in the frequency domain.en_US
dc.description.sponsorshipNational Natural Science Foundation of China [62031014]; Key Research and Development Program of Hainan Province (China) [ZDYF2019195]en_US
dc.description.sponsorshipNationalNatural Science Foundation ofChina, Grant/AwardNumber: 62031014; KeyResearch and Development Program of Hainan Province (China), Grant/AwardNumber: ZDYF2019195en_US
dc.identifier.doi10.1049/cmu2.12269
dc.identifier.endpage509en_US
dc.identifier.issn1751-8628
dc.identifier.issn1751-8636
dc.identifier.issue5en_US
dc.identifier.scopus2-s2.0-85113951239en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.startpage497en_US
dc.identifier.urihttps://doi.org/10.1049/cmu2.12269
dc.identifier.urihttps://hdl.handle.net/20.500.14551/25728
dc.identifier.volume16en_US
dc.identifier.wosWOS:000691392400001en_US
dc.identifier.wosqualityQ4en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherInst Engineering Technology-Ieten_US
dc.relation.ispartofIet Communicationsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subject[No Keywords]en_US
dc.title3D convolutional neural networks based automatic modulation classification in the presence of channel noiseen_US
dc.typeArticleen_US

Dosyalar

Koleksiyon

WoS İndeksli Yayınlar Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu