Deep multi-task learning-based simultaneous channel tap and coefficient estimation

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dc.authorid0000-0001-9474-7372
dc.contributor.authorAygül, Mehmet Ali
dc.contributor.authorÇırpan, Hakan Ali
dc.contributor.authorArslan, Hüseyin
dc.date.accessioned2024-06-12T07:17:06Z
dc.date.available2024-06-12T07:17:06Z
dc.date.issued2024
dc.departmentİstanbul Medipol Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Elektrik ve Elektronik Mühendisliği Bölümü
dc.description.abstractWireless communication systems depend on accurate channel estimation to ensure efficient and reliable data transmission. The channel estimation process consists of two essential steps: channel tap and coefficient estimation. Physical layer features such as time arrival, and signal strengths are well used for the tap estimation. However, prior knowledge is required to use these methods. Recently, machine learning-based methods have been proposed. In particular, deep learning (DL)-based methods are promising because they can learn from raw data without much preprocessing, scale well with extensive and diverse datasets, and capture complex relationships. However, these methods overlook the relationship between the channel taps and coefficients. In this paper, we propose a DL-based multi-task learning method to estimate channel taps and coefficients simultaneously. Simulation results reveal that the performance of the proposed tap estimation method is superior to the traditional DL-based tap estimation. Furthermore, the proposed method removes the need to train two models to estimate channel taps and coefficients.
dc.description.sponsorshipTürkiye Bilimsel ve Teknolojik Araştırma Kurumu (TÜBİTAK)en_US
dc.identifier.citationAygül, M. A., Çırpan, H. A. ve Arslan, H. (2024). Deep multi-task learning-based simultaneous channel tap and coefficient estimation. 6th IEEE Future Networks World Forum, FNWF 2023, Baltimore, November 13-15, 2023. http://dx.doi.org/10.1109/FNWF58287.2023.10520577
dc.identifier.doi10.1109/FNWF58287.2023.10520577
dc.identifier.isbn9798350324587
dc.identifier.issn2770-7660
dc.identifier.scopus2-s2.0-85194133810
dc.identifier.scopusqualityN/A
dc.identifier.urihttp://dx.doi.org/10.1109/FNWF58287.2023.10520577
dc.identifier.urihttps://hdl.handle.net/20.500.12511/12627
dc.identifier.wos001229556600109en_US
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorArslan, Hüseyin
dc.language.isoen
dc.relation.ecinfo:eu-repo/grantAgreement/TUBITAK/SOBAG/119E433
dc.relation.ispartof6th IEEE Future Networks World Forum, FNWF 2023en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectChannel Coefficients
dc.subjectChannel Tap Estimation
dc.subjectDeep Learning
dc.subjectMulti-Task Learning
dc.subjectWireless Channel
dc.titleDeep multi-task learning-based simultaneous channel tap and coefficient estimation
dc.typeConference Object

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