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dc.contributorZola, Enrica Valeria
dc.contributor.authorWang, Min
dc.date.accessioned2017-11-03T13:43:18Z
dc.date.available2018-10-30T01:30:29Z
dc.date.issued2017-10-30
dc.identifier.urihttp://hdl.handle.net/2117/109762
dc.description.abstractThis study focuses on 5G network, which deploys small cells to form multi-hop topologies using high capacity backhaul wireless links to provide localized capacity. Nowadays, high energy efficiency is very important because powering on unnecessarily a massive amount of macro cells or small cells may lead to increased expenses, CO2 emission and environmental destruction. Based on a given MILP that solves the energy consumption optimization problem in a 5G network, this research proposes a heuristic algorithm based on integer relaxation that accelerates the resolution of the MILP. The heuristic algorithm could diminish the route options by striking out the impossible links or links with lower possibility to be used. Our numerical evaluations demonstrate that the proposed algorithm can find very good solutions in short time and has similar performance in terms of energy efficiency over a large number of traffic scenarios.
dc.language.isoeng
dc.publisherUniversitat Politècnica de Catalunya
dc.subjectÀrees temàtiques de la UPC::Enginyeria de la telecomunicació
dc.subject.lcshOptical communications
dc.subject.lcshInternet of things
dc.subject.otherFast heuristics
dc.subject.other5G Optimisation
dc.subject.otherSystem performance
dc.titleFast heuristic algorithm for 5G network energy consumption optimization
dc.typeMaster thesis
dc.subject.lemacComunicacions òptiques
dc.subject.lemacInternet de les coses
dc.subject.lemacComunicació sense fil, Sistemes de
dc.rights.accessRestricted access - author's decision
dc.date.updated2017-10-31T05:24:24Z
dc.audience.educationlevelEstudis de primer/segon cicle
dc.audience.mediatorEscola d'Enginyeria de Telecomunicació i Aeroespacial de Castelldefels
dc.audience.degreeMÀSTER UNIVERSITARI EN APLICACIONS I GESTIÓ DE L'ENGINYERIA DE TELECOMUNICACIÓ (MASTEAM) (Pla 2015)


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