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dc.contributor.authorLuna, Francisco
dc.contributor.authorZapata-Cano, Pablo H.
dc.contributor.authorPalomares-Caballero, Ángel
dc.contributor.authorValenzuela Valdés, Juan F.
dc.date.accessioned2020-03-06T13:25:00Z
dc.date.available2020-03-06T13:25:00Z
dc.date.created2020
dc.date.issued2020-03-06
dc.identifier.urihttps://hdl.handle.net/10630/19370
dc.description.abstractNetwork densification with deployments of many small base stations (SBSs) is a key enabler technology for the fifth generation (5G) cellular networks, and it is also clearly in conflict with one of the target design requirements of 5G systems: a 90% reduction of the power con- sumption. In order to address this issue, switching off a number of SBSs in periods of low traffic demand has been standardized as an recognized strategy to save energy. But this poses a challenging NP-complete opti- mization problem to the system designers, which do also have to provide the users with maxima capacity. This is a multi-objective optimization problem that has been tackled with multi-objective evolutionary algo- rithms (MOEAs). In particular, a problem-specific search operator with problem-domain information has been devised so as to engineer hybrid MOEAs. It is based on promoting solutions that activate SBSs which may serve users with higher data rates, while also deactivating those not serving any user at all. That is, it tries to improve the two problem objectives simultaneously. The resulting hybrid algorithms have shown to reach better approximations to the Pareto fronts than the canonical algorithms over a set of nine scenarios with increasing diversity in SBSs and users.en_US
dc.description.sponsorshipThis work has been supported by the the Spanish and Andalusian goverments, and FEDER, under contrats TIN2016-75097-P, RTI2018-102002-A-I00 and B-TIC-402-UGR18. Francisco Luna also acknowledges support from Universidad de Málaga.en_US
dc.language.isoengen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.rights.urihttp://creativecommons.org/licenses/by-sa/4.0/*
dc.subjectOptimización matemáticaen_US
dc.subjectRedes de antenasen_US
dc.subject.otherProblem specific operatoren_US
dc.subject.otherHybridizationen_US
dc.subject.otherMulti-objective optimizationen_US
dc.subject.otherCell switch-off problemen_US
dc.subject.other5G networksen_US
dc.titleA Capacity-enhanced local search for the 5G cell switch-off problemen_US
dc.typeinfo:eu-repo/semantics/conferenceObjecten_US
dc.centroE.T.S.I. Informáticaen_US
dc.relation.eventtitleInternational Conference in Optimization and Learning (OLA2020)en_US
dc.relation.eventplaceCádiz, Españaen_US
dc.relation.eventdate17/02/2020en_US
dc.rights.ccAtribución-NoComercial-CompartirIgual 4.0 Internacional*
dc.rights.ccAtribución-CompartirIgual 4.0 Internacional*


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