Identification of distorted RF components via deep multi-task learning

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dc.authorid0000-0001-5137-8511
dc.authorid0000-0001-9474-7372
dc.contributor.authorAygül, Mehmet Ali
dc.contributor.authorMemişoğlu, Ebubekir
dc.contributor.authorÇırpan, Hakan Ali
dc.contributor.authorArslan, Hüseyin
dc.date.accessioned2023-02-14T08:32:13Z
dc.date.available2023-02-14T08:32:13Z
dc.date.issued2022
dc.departmentİstanbul Medipol Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Elektrik ve Elektronik Mühendisliği Bölümü
dc.description.abstractHigh-quality radio frequency (RF) components are imperative for efficient wireless communication. However, these components can degrade over time and need to be identified so that either they can be replaced or their effects can be compensated. The identification of these components can be done through observation and analysis of constellation diagrams. However, in the presence of multiple distortions, it is very challenging to isolate and identify the RF components responsible for the degradation. This paper highlights the difficulties of distorted RF components' identification and their importance. Furthermore, a deep multi-task learning algorithm is proposed to identify the distorted components in the challenging scenario. Extensive simulations show that the proposed algorithm can automatically detect multiple distorted RF components with high accuracy in different scenarios.
dc.identifier.citationAygül, M. A., Memişoğlu, E., Çırpan, H. A. ve Arslan, H. (2022). Identification of distorted RF components via deep multi-task learning. IEEE 96th Vehicular Technology Conference (VTC-Fall). London, 26-29 September 2022. https://doi.org/10.1109/VTC2022-Fall57202.2022.10012986
dc.identifier.doi10.1109/VTC2022-Fall57202.2022.10012986
dc.identifier.isbn9781665454681
dc.identifier.issn1550-2252
dc.identifier.scopus2-s2.0-85147035617
dc.identifier.scopusqualityN/A
dc.identifier.urihttps://doi.org/10.1109/VTC2022-Fall57202.2022.10012986
dc.identifier.urihttps://hdl.handle.net/20.500.12511/10430
dc.identifier.volume2022
dc.identifier.wos000927580600291en_US
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorMemişoğlu, Ebubekir
dc.institutionauthorArslan, Hüseyin
dc.language.isoen
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.relation.ispartofIEEE 96th Vehicular Technology Conference (VTC-Fall)en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectDeep Learning
dc.subjectDistorted RF Components Identification
dc.subjectMulti-Task Learning
dc.subjectRF Impairments
dc.titleIdentification of distorted RF components via deep multi-task learning
dc.typeConference Object

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