A waveform parameter assignment framework for 6G with the role of machine learning
| dc.authorid | 0000-0001-9348-9092 | |
| dc.authorid | 0000-0001-9474-7372 | |
| dc.contributor.author | Yazar, Ahmet | |
| dc.contributor.author | Arslan, Hüseyin | |
| dc.date.accessioned | 2021-12-16T07:09:10Z | |
| dc.date.available | 2021-12-16T07:09:10Z | |
| dc.date.issued | 2020 | |
| dc.department | İstanbul Medipol Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Elektrik ve Elektronik Mühendisliği Bölümü | |
| dc.description.abstract | 5G enables a wide variety of wireless communications applications and use cases. There are different requirements associated with the applications, use cases, channel structure, network and user. To meet all of the requirements, several new configurable parameters are defined in 5G New Radio (NR). It is possible that 6G will have even higher number of configurable parameters based on new potential conditions. In line with this trend, configurable waveform parameters are also varied and this variation will increase in 6G considering the potential future necessities. In this paper, association of users and possible configurable waveform parameters in a cell is discussed for 6G communication systems. An assignment framework of configurable waveform parameters with different types of resource allocation optimization mechanisms is proposed. Most of all, the role and usage of machine learning (ML) in this framework is described. A case study with a simulation based dataset generation methodology is also presented. | |
| dc.identifier.citation | Yazar, A. ve Arslan, H. (2020). A waveform parameter assignment framework for 6G with the role of machine learning. IEEE Open Journal of Vehicular Technology, 1, 156-172. https://dx.doi.org/10.1109/OJVT.2020.2992502 | |
| dc.identifier.doi | 10.1109/OJVT.2020.2992502 | |
| dc.identifier.endpage | 172 | |
| dc.identifier.issn | 2644-1330 | |
| dc.identifier.scopusquality | N/A | |
| dc.identifier.startpage | 156 | |
| dc.identifier.uri | https://dx.doi.org/10.1109/OJVT.2020.2992502 | |
| dc.identifier.uri | https://hdl.handle.net/20.500.12511/8679 | |
| dc.identifier.volume | 1 | |
| dc.identifier.wosquality | N/A | |
| dc.indekslendigikaynak | Web of Science | |
| dc.indekslendigikaynak | Scopus | |
| dc.language.iso | en | |
| dc.publisher | IEEE-Institute of Electrical and Electronics Engineers Inc. | |
| dc.relation.ispartof | IEEE Open Journal of Vehicular Technology | en_US |
| dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | |
| dc.relation.tubitak | info:eu-repo/grantAgreement/TUBITAK/SOBAG/119E433 | |
| dc.rights | Attribution 4.0 International | * |
| dc.rights | info:eu-repo/semantics/openAccess | |
| dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | * |
| dc.subject | 6G | |
| dc.subject | Beyond 5G | |
| dc.subject | Machine Learning | |
| dc.subject | Multiple Numerologies | |
| dc.subject | OFDM | |
| dc.subject | Radio Resource Management | |
| dc.subject | Scheduling | |
| dc.subject | Waveform | |
| dc.title | A waveform parameter assignment framework for 6G with the role of machine learning | |
| dc.type | Article |
