Joint estimation of multiple RF impairments using 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.authorArslan, Hüseyin
dc.date.accessioned2022-08-11T12:49:21Z
dc.date.available2022-08-11T12:49:21Z
dc.date.issued2022
dc.departmentİstanbul Medipol Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Elektrik ve Elektronik Mühendisliği Bölümü
dc.description.abstractRadio-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.
dc.identifier.citationAygül, 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
dc.identifier.doi10.1109/WCNC51071.2022.9771740
dc.identifier.endpage2398
dc.identifier.isbn9781665442664
dc.identifier.issn1525-3511
dc.identifier.scopus2-s2.0-85130735840
dc.identifier.scopusqualityN/A
dc.identifier.startpage2393
dc.identifier.urihttps://doi.org/10.1109/WCNC51071.2022.9771740
dc.identifier.urihttps://hdl.handle.net/20.500.12511/9643
dc.identifier.wos000819473100403en_US
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorMemişoğlu, Ebubekir
dc.institutionauthorArslan, Hüseyin
dc.language.isoen
dc.publisherIEEE-Institute of Electrical and Electronics Engineers Inc.
dc.relation.ispartofIEEE Wireless Communications and Networking Conference (IEEE WCNC)en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanı
dc.relation.tubitakinfo:eu-repo/grantAgreement/TUBITAK/SOBAG/5200030
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectDeep Learning
dc.subjectJoint Estimation
dc.subjectMulti-Task Learning
dc.subjectMultiple RF Impairments
dc.titleJoint estimation of multiple RF impairments using deep multi-task learning
dc.typeConference Object

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