Centralized and decentralized ml-enabled integrated terrestrial and non-terrestrial networks

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dc.authorid0000-0002-8376-0536
dc.authorid0000-0001-9474-7372
dc.contributor.authorAygül, Mehmet Ali
dc.contributor.authorTürkmen, Halise
dc.contributor.authorSağlam, Mehmet İzzet
dc.contributor.authorÇırpan, Hakan Ali
dc.contributor.authorArslan, Hüseyin
dc.date.accessioned2024-06-12T07:04:16Z
dc.date.available2024-06-12T07:04:16Z
dc.date.issued2024
dc.departmentİstanbul Medipol Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Elektrik ve Elektronik Mühendisliği Bölümü
dc.description.abstractNon-terrestrial networks (NTNs) are a critical enabler of the persistent connectivity vision of sixth-generation networks, as they can service areas where terrestrial infrastructure falls short. However, the integration of these networks with the terrestrial network is laden with obstacles. The dynamic nature of NTN communication scenarios and numerous variables render conventional model-based solutions computationally costly and impractical for resource allocation and parameter optimization. Machine learning (ML)-based solutions can perform a pivotal role due to their inherent ability to uncover the hidden patterns in time-varying, multi-dimensional data with superior performance and less complexity. Centralized ML (CML) and decentralized ML (DML), named so based on the distribution of the data and computational load, are two classes of ML that are being studied as solutions for the various complications of terrestrial and non-terrestrial networks (TNTN) integration. Both have their benefits and drawbacks under different circumstances, and it is integral to choose the appropriate ML approach for each TNTN integration issue. To this end, this paper goes over the TNTN integration architectures as given in the 3GPP standard releases, proposing possible scenarios. Then, the capabilities and challenges of CML and DML are explored from the vantage point of these scenarios.
dc.identifier.citationAygül, M. A., Türkmen, H., Sağlam, M. İ., Çırpan, H. A. ve Arslan, H. (2024). Centralized and decentralized ml-enabled integrated terrestrial and non-terrestrial networks. 6th IEEE Future Networks World Forum, FNWF 2023, Baltimore, November 13-15, 2023. http://dx.doi.org/10.1109/FNWF58287.2023.10520439
dc.identifier.doi10.1109/FNWF58287.2023.10520439
dc.identifier.isbn9798350324587
dc.identifier.issn2770-7660
dc.identifier.scopus2-s2.0-85194147209
dc.identifier.scopusqualityN/A
dc.identifier.urihttp://dx.doi.org/10.1109/FNWF58287.2023.10520439
dc.identifier.urihttps://hdl.handle.net/20.500.12511/12626
dc.identifier.wos001229556600047en_US
dc.indekslendigikaynakScopus
dc.institutionauthorTürkmen, Halise
dc.institutionauthorArslan, Hüseyin
dc.language.isoen
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.subjectCentralized Learning
dc.subjectDecentralized Learning
dc.subjectIntegrated Terrestrial And Non-Terrestrial Networks
dc.subjectMachine Learning
dc.subjectNon-Terrestrial Networks
dc.titleCentralized and decentralized ml-enabled integrated terrestrial and non-terrestrial networks
dc.typeConference Object

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