Exploring Deep Learning Architectures for RF Signal Classification

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dc.contributor.authorPolák, Ladislavcs
dc.contributor.authorTurák, Samuelcs
dc.contributor.authorŠotner, Romancs
dc.contributor.authorKufa, Jancs
dc.contributor.authorMaršálek, Romancs
dc.contributor.authorDhaka, Arvindcs
dc.date.accessioned2025-09-15T11:55:42Z
dc.date.available2025-09-15T11:55:42Z
dc.date.issued2025-05-12cs
dc.description.abstractFuture 6G radio networks will heavily rely on deep learning (DL) models for both signal and data processing. DL-based solutions can be highly effective in classifying various radio frequency (RF) signals influenced by noise or intentional jamming as they are capable of recognizing patterns even under challenging conditions. This paper focuses on the classification of different RF signals using three DL-based models: CNN, GRU, and CGDNN. For this purpose, a dataset containing RF signals influenced by various impairments (e.g., I/Q-imbalance) and transmission conditions (e.g., multipath propagation) was created using MATLAB. Both the dataset and the source code have been made publicly available to support further research in this area. Preliminary results shown that the performance of DL-based approaches depends not only on the RF impairments considered but also on the preparation of the dataset.en
dc.formattextcs
dc.format.extent1-6cs
dc.format.mimetypeapplication/pdfcs
dc.identifier.citation35th International Conference Radioelektronika. 2025, p. 1-6.en
dc.identifier.doi10.1109/RADIOELEKTRONIKA65656.2025.11008396cs
dc.identifier.isbn979-8-3315-4447-8cs
dc.identifier.orcid0000-0001-7084-6210cs
dc.identifier.orcid0000-0002-2430-1815cs
dc.identifier.orcid0000-0002-3784-5707cs
dc.identifier.orcid0000-0003-2926-5507cs
dc.identifier.other198734cs
dc.identifier.researcheridG-4209-2017cs
dc.identifier.scopus36167253100cs
dc.identifier.scopus21834721500cs
dc.identifier.scopus56880305800cs
dc.identifier.urihttps://hdl.handle.net/11012/255541
dc.language.isoencs
dc.publisherIEEEcs
dc.relation.ispartof35th International Conference Radioelektronikacs
dc.relation.urihttps://ieeexplore.ieee.org/document/11008396cs
dc.rights(C) IEEEcs
dc.rights.accessopenAccesscs
dc.subjectClassificationen
dc.subjectChannel modelsen
dc.subjectDataseten
dc.subjectDeep learningen
dc.subjectNeural networksen
dc.subjectRF impairmentsen
dc.subjectRF signalsen
dc.titleExploring Deep Learning Architectures for RF Signal Classificationen
dc.type.driverconferenceObjecten
dc.type.statusPeer-revieweden
dc.type.versionacceptedVersionen
eprints.grantNumberinfo:eu-repo/grantAgreement/MSM/LU/LUC24141cs
sync.item.dbidVAV-198734en
sync.item.dbtypeVAVen
sync.item.insts2025.09.15 13:55:42en
sync.item.modts2025.09.15 13:33:20en
thesis.grantorVysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií. Ústav radioelektronikycs
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