Platt, StevenDemirel, BerkayOliver Riera, Miquel2023-03-242023-03-242021Platt 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: 10.1109/5GWF52925.2021.00081http://hdl.handle.net/10230/56342Comunicació presentada a 2021 IEEE 4th 5G World Forum (5GWF 2021), celebrat del 13 al 15 d'octubre de 2021 de manera virtual.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.application/pdfeng© 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/10.1109/5GWF52925.2021.00081info:eu-repo/semantics/conferenceObjecthttp://dx.doi.org/10.1109/5GWF52925.2021.000815G6Gmachine learningblockchainmobility managementinfo:eu-repo/semantics/openAccess