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dc.contributor.authorMcClellan, Miranda
dc.contributor.authorCervelló Pastor, Cristina
dc.contributor.authorSallent Ribes, Sebastián
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament d'Enginyeria Telemàtica
dc.date.accessioned2021-04-12T11:22:44Z
dc.date.available2021-04-12T11:22:44Z
dc.date.issued2020-07-09
dc.identifier.citationMcClellan, M.; Cervelló-Pastor, C.; Sallent, S. Deep learning at the mobile edge: Opportunities for 5G networks. "Applied sciences", 9 Juliol 2020, vol. 10, núm. 14, p. 1-27.
dc.identifier.issn2076-3417
dc.identifier.urihttp://hdl.handle.net/2117/343529
dc.description.abstractMobile edge computing (MEC) within 5G networks brings the power of cloud computing, storage, and analysis closer to the end-user. The increased speeds and reduced delay enable novel applications such as connected vehicles, large-scale IoT, video streaming, and industry robotics. Machine Learning (ML) is leveraged within mobile edge computing to predict changes in demand based on cultural events, natural disasters, or daily commute patterns, and it prepares the network by automatically scaling up network resources as needed. Together, mobile edge computing andML enable seamless automation of network management to reduce operational costs and enhance user experience. In this paper, we discuss the state of the art for ML within mobile edge computing and the advances needed in automating adaptive resource allocation, mobility modeling, security, and energy efficiency for 5G networks
dc.format.extent27 p.
dc.language.isoeng
dc.publisherMultidisciplinary Digital Publishing Institute
dc.rightsAttribution 3.0 Spain
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/es/
dc.subjectÀrees temàtiques de la UPC::Enginyeria de la telecomunicació::Telemàtica i xarxes d'ordinadors
dc.subject.lcsh5G mobile communication systems
dc.subject.other5G
dc.subject.otheredge network
dc.subject.otherdeep learning
dc.subject.otherreinforcement learning
dc.subject.othercaching
dc.subject.othertask offloading
dc.subject.othermobile computing
dc.subject.otheredge computing
dc.subject.othermobile edge computing
dc.subject.othercloud computing
dc.subject.othernetwork function virtualization
dc.subject.otherslicing
dc.subject.other5G network standardization
dc.titleDeep learning at the mobile edge: Opportunities for 5G networks
dc.typeArticle
dc.subject.lemacComunicacions mòbils, Sistemes de
dc.contributor.groupUniversitat Politècnica de Catalunya. BAMPLA - Disseny i Avaluació de Xarxes i Serveis de Banda Ampla
dc.identifier.doi10.3390/app10144735
dc.relation.publisherversionhttps://www.mdpi.com/2076-3417/10/14/4735
dc.rights.accessOpen Access
local.identifier.drac29569914
dc.description.versionPostprint (published version)
dc.relation.projectidinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2019-108713RB-C51/ES/EVOLUCION HACIA REDES Y SERVICIOS AUTO-GESTIONADOS PARA EL 5G DEL FUTURO/
local.citation.authorMcClellan, M.; Cervelló-Pastor, C.; Sallent, S.
local.citation.publicationNameApplied sciences
local.citation.volume10
local.citation.number14
local.citation.startingPage1
local.citation.endingPage27


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