dc.contributor.author | Aygül, Mehmet Ali | |
dc.contributor.author | Memişoğlu, Ebubekir | |
dc.contributor.author | Çırpan, Hakan Ali | |
dc.contributor.author | Arslan, Hüseyin | |
dc.date.accessioned | 2023-02-14T08:32:13Z | |
dc.date.available | 2023-02-14T08:32:13Z | |
dc.date.issued | 2022 | en_US |
dc.identifier.citation | Aygül, 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 | en_US |
dc.identifier.isbn | 9781665454681 | |
dc.identifier.issn | 1550-2252 | |
dc.identifier.uri | https://doi.org/10.1109/VTC2022-Fall57202.2022.10012986 | |
dc.identifier.uri | https://hdl.handle.net/20.500.12511/10430 | |
dc.description.abstract | High-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. | en_US |
dc.language.iso | eng | en_US |
dc.publisher | Institute of Electrical and Electronics Engineers Inc. | en_US |
dc.rights | info:eu-repo/semantics/openAccess | en_US |
dc.subject | Deep Learning | en_US |
dc.subject | Distorted RF Components Identification | en_US |
dc.subject | Multi-Task Learning | en_US |
dc.subject | RF Impairments | en_US |
dc.title | Identification of distorted RF components via deep multi-task learning | en_US |
dc.type | conferenceObject | en_US |
dc.relation.ispartof | IEEE 96th Vehicular Technology Conference (VTC-Fall) | en_US |
dc.department | İstanbul Medipol Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Elektrik ve Elektronik Mühendisliği Bölümü | en_US |
dc.authorid | 0000-0001-5137-8511 | en_US |
dc.authorid | 0000-0001-9474-7372 | en_US |
dc.identifier.volume | 2022 | en_US |
dc.relation.publicationcategory | Konferans Öğesi - Uluslararası - Kurum Öğretim Elemanı | en_US |
dc.identifier.doi | 10.1109/VTC2022-Fall57202.2022.10012986 | en_US |
dc.institutionauthor | Memişoğlu, Ebubekir | |
dc.institutionauthor | Arslan, Hüseyin | |
dc.identifier.wos | 000927580600291 | en_US |
dc.identifier.scopus | 2-s2.0-85147035617 | en_US |