Using transition learning to enhance mobile-controlled handover in decentralized future networks

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• dc.contributor.author Platt, Steven
• dc.contributor.author Demirel, Berkay
• dc.contributor.author Oliver Riera, Miquel
• dc.date.accessioned 2023-03-24T07:24:00Z
• dc.date.available 2023-03-24T07:24:00Z
• dc.date.issued 2021
• dc.description Comunicació presentada a 2021 IEEE 4th 5G World Forum (5GWF 2021), celebrat del 13 al 15 d'octubre de 2021 de manera virtual.
• dc.description.abstract Traditionally, resource management and capacity allocation has been controlled network-side in cellular deployment. As autonomicity has been added to network design, machine learning technologies have largely followed this paradigm, benefiting from the higher compute capacity and informational context available at the network core. However, when these network services are disaggregated or decentralized, models that rely on assumed levels of network or information availability may no longer function reliably. This paper presents an inverted view of the resource management paradigm; one in which the client device executes a learning algorithm and manages its own mobility under a scenario where the networks and their corresponding data underneath are not being centrally managed.
• dc.description.sponsorship This work was partially supported by the Spanish and Catalan Governments through the project ”Plan Nacional”: AEI/FEDER TEC2016-79510 ”Redes Con Celdas Densas y Masivas” and the SGR2017-2019.
• dc.format.mimetype application/pdf
• dc.identifier.citation Platt S, Demirel B, Oliver M. Using transition learning to enhance mobile-controlled handover in decentralized future networks. In: 2021 IEEE 4th 5G World Forum (5GWF 2021); 2021 Oct 13-15; Montreal, Canada. [Piscataway]: IEEE; 2021. p. 424-9. DOI: 10.1109/5GWF52925.2021.00081
• dc.identifier.doi http://dx.doi.org/10.1109/5GWF52925.2021.00081
• dc.identifier.uri http://hdl.handle.net/10230/56342
• dc.language.iso eng
• dc.publisher Institute of Electrical and Electronics Engineers (IEEE)
• dc.relation.ispartof 2021 IEEE 4th 5G World Forum (5GWF 2021); 2021 Oct 13-15; Montreal, Canada. [Piscataway]: IEEE; 2021. p. 424-9.
• dc.relation.projectID info:eu-repo/grantAgreement/ES/1PE/TEC2016-79510
• dc.rights © 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. http://dx.doi.org/10.1109/5GWF52925.2021.00081
• dc.rights.accessRights info:eu-repo/semantics/openAccess
• dc.subject.keyword 5G
• dc.subject.keyword 6G
• dc.subject.keyword machine learning
• dc.subject.keyword blockchain
• dc.subject.keyword mobility management
• dc.title Using transition learning to enhance mobile-controlled handover in decentralized future networks
• dc.type info:eu-repo/semantics/conferenceObject
• dc.type.version info:eu-repo/semantics/acceptedVersion