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dc.contributor.authorAlbonda, Haider Daami Resin
dc.contributor.authorPérez Romero, Jordi
dc.contributor.otherUniversitat Politècnica de Catalunya. Doctorat en Teoria del Senyal i Comunicacions
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament de Teoria del Senyal i Comunicacions
dc.date.accessioned2020-10-02T14:34:54Z
dc.date.available2020-10-02T14:34:54Z
dc.date.issued2020-03
dc.identifier.citationAlbonda, H.; Perez-Romero, J. Analysis of RAN slicing for cellular V2X and mobile broadband services based on reinforcement learning. "EAI Endorsed Transactions on Wireless Spectrum", Març 2020, vol. 4, núm. 13, p. 1-11.
dc.identifier.issn2312-6620
dc.identifier.urihttp://hdl.handle.net/2117/329738
dc.description.abstractRadio Access Network (RAN) slicing is one of the key enablers to provide the design flexibility and enable 5G system to support heterogeneous services over a common platform (i.e., by creating a customized slice for each service). In this regard, this paper provides an analysis of a Reinforcement Learning (RL)-based RAN slicing strategy for a heterogeneous network with two generic services of 5G, namely enhanced mobile broadband (eMBB) and vehicle-to-everything (V2X). In particular, this paper investigates the RAN slicing by evaluating the proposed scheme under different algorithm configurations (i.e., number of actions of RL) and parameters in order to analyze the performance in terms of metrics such as RL convergence time and to demonstrate the capability of the algorithm to perform an efficient allocation of resources among slices. In addition, this study compares the results obtained by the proposed solution to those obtained with a Proportional Scheme.
dc.description.sponsorshipThis work was supported in part by the Spanish Research Council and FEDER Funds under SONAR 5G Grant with reference TEC2017-82651-R, and in part by the Baghdad University of Technology
dc.format.extent11 p.
dc.language.isoeng
dc.publisherEuropean Alliance for Innovation n.o.
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 Spain
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subjectÀrees temàtiques de la UPC::Enginyeria de la telecomunicació::Radiocomunicació i exploració electromagnètica::Comunicacions mòbils
dc.subjectÀrees temàtiques de la UPC::Enginyeria de la telecomunicació::Telemàtica i xarxes d'ordinadors
dc.subject.lcshMobile communication systems
dc.subject.lcshComputer networks
dc.subject.otherVehicle-to-everything (V2X)
dc.subject.otherReinforcement learning
dc.subject.otherNetwork slicing
dc.subject.otherRAN slicing
dc.titleAnalysis of RAN slicing for cellular V2X and mobile broadband services based on reinforcement learning
dc.typeArticle
dc.subject.lemacComunicacions mòbils, Sistemes de
dc.subject.lemacOrdinadors, Xarxes d'
dc.contributor.groupUniversitat Politècnica de Catalunya. GRCM - Grup de Recerca en Comunicacions Mòbils
dc.identifier.doi10.4108/eai.13-7-2018.163841
dc.description.peerreviewedPeer Reviewed
dc.relation.publisherversionhttps://eudl.eu/doi/10.4108/eai.13-7-2018.163841
dc.rights.accessOpen Access
local.identifier.drac28997515
dc.description.versionPostprint (published version)
dc.relation.projectidinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/TEC2017-82651-R/ES/SOFTWARIZACION Y OPTIMIZACION AUTOMATICA DE REDES DE ACCESO RADIO 5G MULTI-TENANT/
local.citation.authorAlbonda, H.; Perez-Romero, J.
local.citation.publicationNameEAI Endorsed Transactions on Wireless Spectrum
local.citation.volume4
local.citation.number13
local.citation.startingPage1
local.citation.endingPage11


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Except where otherwise noted, content on this work is licensed under a Creative Commons license : Attribution-NonCommercial-NoDerivs 3.0 Spain