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Learning-based tracking area list management in 4G and 5G networks
dc.contributor.author | Moysen Cortes, Jessica |
dc.contributor.author | García Lozano, Mario |
dc.contributor.other | Universitat Politècnica de Catalunya. Departament de Teoria del Senyal i Comunicacions |
dc.date.accessioned | 2020-07-23T07:00:32Z |
dc.date.available | 2020-07-23T07:00:32Z |
dc.date.issued | 2020-08-01 |
dc.identifier.citation | Moysen, J.; Garcia-Lozano, M. Learning-based tracking area list management in 4G and 5G networks. "IEEE transactions on mobile computing", 1 Agost 2020, vol. 19, núm. 8, p. 1862-1878. |
dc.identifier.issn | 1536-1233 |
dc.identifier.other | https://ieeexplore.ieee.org/document/8706674 |
dc.identifier.uri | http://hdl.handle.net/2117/327429 |
dc.description | © 2020 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 |
dc.description.abstract | Mobility management in 5G networks is a very challenging issue. It requires novel ideas and improved management so that signaling is kept minimized and far from congesting the network. Mobile networks have become massive generators of data and in the forthcoming years this data is expected to increase drastically. The use of intelligence and analytics based on big data is a good ally for operators to enhance operational efficiency and provide individualized services. This work proposes to exploit User Equipment (UE) patterns and hidden relationships from geo-spatial time series to minimize signaling due to idle mode mobility. We propose a holistic methodology to generate optimized Tracking Area Lists (TALs) in a per UE manner, considering its learned individual behavior. The k -means algorithm is proposed to find the allocation of cells into tracking areas. This is used as a basis for the TALs optimization itself, which follows a combined multi-objective and single-objective approach depending on the UE behavior. The last stage identifies UE profiles and performs the allocation of the TAL by using a neural network. The goodness of each technique has been evaluated individually and jointly under very realistic conditions and different situations. Results demonstrate important signaling reductions and good sensitivity to changing conditions. |
dc.description.sponsorship | This work was supported by the Spanish National Science Council and ERFD funds under projects TEC2014-60258-C2-2-R and RTI2018-099880-B-C32. |
dc.format.extent | 17 p. |
dc.language.iso | eng |
dc.subject | Àrees temàtiques de la UPC::Enginyeria de la telecomunicació |
dc.subject.lcsh | Wireless communication systems |
dc.subject.other | Mobility management |
dc.subject.other | tracking area lists |
dc.subject.other | mobile networks |
dc.subject.other | big data analytics |
dc.subject.other | multi-objective optimization |
dc.title | Learning-based tracking area list management in 4G and 5G networks |
dc.type | Article |
dc.subject.lemac | Xarxes locals sense fil Wi-Fi |
dc.contributor.group | Universitat Politècnica de Catalunya. WiComTec - Grup de recerca en Tecnologies i Comunicacions Sense Fils |
dc.identifier.doi | 10.1109/TMC.2019.2915079 |
dc.description.peerreviewed | Peer Reviewed |
dc.relation.publisherversion | IEEE |
dc.rights.access | Open Access |
local.identifier.drac | 28807350 |
dc.description.version | Postprint (author's final draft) |
dc.relation.projectid | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/RTI2018-099880-B-C32/ES/ROBOTICA EN LA NUBE Y EL IMPACTO DE LAS REDES 5G EN LA FABRICA DEL FUTURO. SUBPROYECTO UPC./ |
dc.relation.projectid | info:eu-repo/grantAgreement/MINECO/FEDER/TEC2014/60258-C2-2-R |
local.citation.author | Moysen, J.; Garcia-Lozano, M. |
local.citation.publicationName | IEEE transactions on mobile computing |
local.citation.volume | 19 |
local.citation.number | 8 |
local.citation.startingPage | 1862 |
local.citation.endingPage | 1878 |
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