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5GMEDE-5G mobile edge computing with enriched radio network information services

2020-09-30Conference object Open access
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dc.contributor.authorAkman, A.
dc.contributor.authorMonteiro, V.
dc.contributor.authorMarques, P.
dc.contributor.authorRodriguez, J.
dc.contributor.authorVieira, Pedro
dc.contributor.authorMarques, H.
dc.contributor.authorAbdalla, A.
dc.date.accessioned2021-04-30T09:48:36Z
dc.date.available2021-04-30T09:48:36Z
dc.date.issued2020-09-30
dc.description.abstract5G has a number of challenges to solve. Higher data usage and processing power necessities have emerged from the increasing number of mobile users as well as mobile applications becoming more and more demanding. There is a significant need to reduce the latency of the mobile network while cutting down on energy consumption. Backhaul traffic needs to be optimized to avoid setting up costly backhaul connections. Many loT scenarios have conflicting requirements; the need for cheap and low complexity devices vs the need for processing power. Operators need to come up with value added services to avoid being dumb-pipe operators. The answer to these challenges and more require cloud-computing capabilities within the Radio :Access Network (RAN) as well as a platform for mobile operators and third-party application providers to utilize these computing capabilities. This is where Mobile Edge Computing (MEC), one of the key emerging technologies for 5G, comes into play. The 5GMEDE project proposes to not only develop and demonstrate a complete MEC solution, including the MEC framework, Base Station services and applications to run on top, but also offers innovative features like constraint based mobile edge selection, using data analytics to enrich and refine real-time radio network information and utilizing Software Defined Wireless Networks (SDWN) concepts to improve mobility and resource allocation services to enable operators.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationAKMAN, A.; [et al] – 5GMEDE-5G Mobile Edge Computing with Enriched Radio Network Information Services. In 2020 IEEE 25th International Workshop on Computer Aided Modeling and Design of Communication Links and Networks (CAMAD). Pisa, Italy: IEEE, 2020. ISBN 978-1-7281-6339-0. Pp. 1-5pt_PT
dc.identifier.doi10.1109/CAMAD50429.2020.9209310pt_PT
dc.identifier.eissn2378-4873
dc.identifier.isbn978-1-7281-6339-0
dc.identifier.isbn978-1-7281-6340-6
dc.identifier.issn2378-4865
dc.identifier.urihttp://hdl.handle.net/10400.21/13254
dc.language.isoengpt_PT
dc.publisherIEEEpt_PT
dc.relation33583 - European Funds (FEDER) by PT2020 of ANIpt_PT
dc.relation.publisherversionhttps://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9209310&tag=1pt_PT
dc.subjectMECpt_PT
dc.subject5Gpt_PT
dc.subjectEnergy efficiencypt_PT
dc.subjectRANpt_PT
dc.title5GMEDE-5G mobile edge computing with enriched radio network information servicespt_PT
dc.typeconference object
dspace.entity.typePublication
oaire.citation.conferencePlace14-16 Sept. 2020 - Pisa, Italypt_PT
oaire.citation.endPage5pt_PT
oaire.citation.startPage1pt_PT
oaire.citation.title2020 IEEE 25th International Workshop on Computer Aided Modeling and Design of Communication Links and Networks (CAMAD)pt_PT
person.familyNameVieira
person.givenNamePedro
person.identifier.ciencia-id071B-9A70-15B8
person.identifier.orcid0000-0003-0279-8741
person.identifier.scopus-author-id7004567421
rcaap.rightsopenAccesspt_PT
rcaap.typeconferenceObjectpt_PT
relation.isAuthorOfPublication51ae3527-d4ea-46f4-b6c9-62c7b77ac728
relation.isAuthorOfPublication.latestForDiscovery51ae3527-d4ea-46f4-b6c9-62c7b77ac728

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