Selection of waveform parameters using machine learning for 5G and beyond

Basit Öğe Kaydı
dc.authorid0000-0001-9348-9092
dc.authorid0000-0001-9474-7372
dc.contributor.authorYazar, Ahmet
dc.contributor.authorArslan, Hüseyin
dc.date.accessioned2020-01-30T09:01:04Z
dc.date.available2020-01-30T09:01:04Z
dc.date.issued2019
dc.departmentİstanbul Medipol Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Elektrik ve Elektronik Mühendisliği Bölümü
dc.description.abstractFlexibility is one of the essential requirements for future cellular communications technologies. Providing customized communications solutions for each user and service type cannot be possible without the flexibility in 5G and beyond. Different optimizations need to be done for the flexibility related structures of 5G and beyond systems. In this paper, a novel machine learning (ML) based selection mechanism for the configurable waveform parameters is designed from the flexibility perspective. Moreover, a simulation based dataset generation methodology is proposed for ML systems. Results of computer simulations are presented using the generated dataset.
dc.identifier.citationYazar, A. ve Arslan, H. (2019). Selection of waveform parameters using machine learning for 5G and beyond. 30th IEEE Annual International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC. Istanbul: Turkey, 8-11 September 2019. http://doi.org/10.1109/PIMRC.2019.8904153
dc.identifier.doi10.1109/PIMRC.2019.8904153
dc.identifier.isbn9781538681107
dc.identifier.scopusqualityN/A
dc.identifier.urihttp://doi.org/10.1109/PIMRC.2019.8904153
dc.identifier.urihttps://hdl.handle.net/20.500.12511/4933
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.relation.ispartof30th IEEE Annual International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRCen_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/embargoedAccess
dc.subject5G and Beyond
dc.subjectMachine Learning
dc.subjectMulti-Numerology
dc.subjectResource Allocation
dc.subjectWaveform
dc.titleSelection of waveform parameters using machine learning for 5G and beyond
dc.typeConference Object

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