dc.contributor.author | Aygül, Mehmet Ali | |
dc.contributor.author | Türkmen, Halise | |
dc.contributor.author | Sağlam, Mehmet İzzet | |
dc.contributor.author | Çırpan, Hakan Ali | |
dc.contributor.author | Arslan, Hüseyin | |
dc.date.accessioned | 2024-06-12T07:04:16Z | |
dc.date.available | 2024-06-12T07:04:16Z | |
dc.date.issued | 2023 | en_US |
dc.identifier.citation | Aygül, M. A., Türkmen, H., Sağlam, M. İ., Çırpan, H. A. ve Arslan, H. (2023). 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 | en_US |
dc.identifier.isbn | 9798350324587 | |
dc.identifier.uri | http://dx.doi.org/10.1109/FNWF58287.2023.10520439 | |
dc.identifier.uri | https://hdl.handle.net/20.500.12511/12626 | |
dc.description.abstract | Non-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. | en_US |
dc.language.iso | eng | en_US |
dc.rights | info:eu-repo/semantics/openAccess | en_US |
dc.subject | Centralized Learning | en_US |
dc.subject | Decentralized Learning | en_US |
dc.subject | Integrated Terrestrial And Non-Terrestrial Networks | en_US |
dc.subject | Machine Learning | en_US |
dc.subject | Non-Terrestrial Networks | en_US |
dc.title | Centralized and decentralized ml-enabled integrated terrestrial and non-terrestrial networks | en_US |
dc.type | conferenceObject | en_US |
dc.relation.ispartof | 6th IEEE Future Networks World Forum, FNWF 2023 | en_US |
dc.department | İstanbul Medipol Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Elektrik ve Elektronik Mühendisliği Bölümü | en_US |
dc.authorid | 0000-0002-8376-0536 | en_US |
dc.authorid | 0000-0001-9474-7372 | en_US |
dc.relation.publicationcategory | Konferans Öğesi - Uluslararası - Kurum Öğretim Elemanı | en_US |
dc.identifier.doi | 10.1109/FNWF58287.2023.10520439 | en_US |
dc.institutionauthor | Türkmen, Halise | |
dc.institutionauthor | Arslan, Hüseyin | |
dc.identifier.scopus | 2-s2.0-85194147209 | en_US |