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Automatic tunning of Okumura-Hata model on railway communications

dc.contributor.authorBeire, Ana Rita
dc.contributor.authorCota, Nuno
dc.contributor.authorPinheiro Pita, Helder Jorge
dc.contributor.authorRodrigues, António
dc.date.accessioned2015-08-17T16:19:53Z
dc.date.available2015-08-17T16:19:53Z
dc.date.issued2014
dc.description.abstractThis paper presents the Genetic Algorithms (GA) as an efficient solution for the Okumura-Hata prediction model tuning on railways communications. A method for modelling the propagation model tuning parameters was presented. The algorithm tuning and validation were based on real networks measurements carried out on four different propagation scenarios and several performance indicators were used. It was shown that the proposed GA is able to produce significant improvements over the original model. The algorithm developed is currently been used on real GSM-R network planning process for an enhanced resources usage.por
dc.identifier.citationBEIRE, Ana Rita; [et al] – Automatic tunning of Okumura-Hata model on railway communications. In Wireless Personal Communications. SPRINGER, 2014. ISSN: 0929-6212. p. 562-567.
dc.identifier.doi10.1109/WPMC.2014.7014881
dc.identifier.issn0929-6212
dc.identifier.issn1572-834X
dc.identifier.urihttp://hdl.handle.net/10400.21/4788
dc.language.isoengpor
dc.peerreviewedyespor
dc.publisherSpringerpor
dc.subjectGenetic Algorithmpor
dc.subjectOkumura-Hatapor
dc.subjectPropagation Modelpor
dc.subjectRailway Communicationspor
dc.subjectTuningpor
dc.titleAutomatic tunning of Okumura-Hata model on railway communicationspor
dc.typejournal article
dspace.entity.typePublication
oaire.citation.titleWireless Personal Communicationspor
person.familyNameCota
person.familyNamePinheiro Pita
person.givenNameNuno
person.givenNameHelder Jorge
person.identifier.orcid0000-0001-7023-7044
person.identifier.orcid0000-0002-7483-4257
rcaap.rightsclosedAccesspor
rcaap.typearticlepor
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relation.isAuthorOfPublicationbe663ed9-4e03-4506-9c3e-50017cea7d0d
relation.isAuthorOfPublication.latestForDiscoverybe663ed9-4e03-4506-9c3e-50017cea7d0d

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