dc.contributor.author | Aygül, Mehmet Ali | |
dc.contributor.author | Memişoğlu, Ebubekir | |
dc.contributor.author | Arslan, Hüseyin | |
dc.date.accessioned | 2022-08-11T12:49:21Z | |
dc.date.available | 2022-08-11T12:49:21Z | |
dc.date.issued | 2022 | en_US |
dc.identifier.citation | Aygül, M. A., Memişoğlu, E. ve Arslan, H. (2022). Joint estimation of multiple RF impairments using deep multi-task learning. IEEE Wireless Communications and Networking Conference (IEEE WCNC) içinde (2393-2398. ss.). Austin, TX, April 10-13, 2022. https://doi.org/10.1109/WCNC51071.2022.9771740 | en_US |
dc.identifier.isbn | 9781665442664 | |
dc.identifier.issn | 1525-3511 | |
dc.identifier.uri | https://doi.org/10.1109/WCNC51071.2022.9771740 | |
dc.identifier.uri | https://hdl.handle.net/20.500.12511/9643 | |
dc.description.abstract | Radio-frequency (RF) front-end forms a critical part of any radio system, defining its cost as well as communication performance. However, these components frequently exhibit non-ideal behavior, referred to as impairments, due to the imperfections in the manufacturing/design process. Most of the designers rely on simplified closed-form models to estimate these impairments. On the other hand, these models do not holistically or accurately capture the effects of real-world RF front-end components. Recently, machine learning-based algorithms have been proposed to estimate these impairments. However, these algorithms are not capable of estimating multiple RF impairments jointly, which leads to limited estimation accuracy. In this paper, the joint estimation of multiple RF impairments by exploiting the relationship between them is proposed. To do this, a deep multi-task learning-based algorithm is designed. Extensive simulation results reveal that the performance of the proposed joint RF impairments estimation algorithm is superior to the conventional individual estimations in terms of mean-square error. Moreover, the proposed algorithm removes the need of training multiple models for estimating the different impairments. | en_US |
dc.language.iso | eng | en_US |
dc.publisher | IEEE-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 | Joint Estimation | en_US |
dc.subject | Multi-Task Learning | en_US |
dc.subject | Multiple RF Impairments | en_US |
dc.title | Joint estimation of multiple RF impairments using deep multi-task learning | en_US |
dc.type | conferenceObject | en_US |
dc.relation.ispartof | IEEE Wireless Communications and Networking Conference (IEEE WCNC) | 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.startpage | 2393 | en_US |
dc.identifier.endpage | 2398 | en_US |
dc.relation.tubitak | info:eu-repo/grantAgreement/TUBITAK/SOBAG/5200030 | |
dc.relation.publicationcategory | Konferans Öğesi - Uluslararası - Kurum Öğretim Elemanı | en_US |
dc.identifier.doi | 10.1109/WCNC51071.2022.9771740 | en_US |
dc.institutionauthor | Memişoğlu, Ebubekir | |
dc.institutionauthor | Arslan, Hüseyin | |
dc.identifier.wos | 000819473100403 | en_US |
dc.identifier.scopus | 2-s2.0-85130735840 | en_US |