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A mobile network planning tool based on data analytics
dc.contributor.author | Moysen Cortes, Jessica |
dc.contributor.author | Giupponi, Lorenza |
dc.contributor.author | Mangues Bafalluy, Josep |
dc.contributor.other | Universitat Politècnica de Catalunya. Departament de Teoria del Senyal i Comunicacions |
dc.date.accessioned | 2017-10-31T10:29:51Z |
dc.date.available | 2017-10-31T10:29:51Z |
dc.date.issued | 2017-02-05 |
dc.identifier.citation | Moysen, J., Giupponi, L., J. M. A mobile network planning tool based on data analytics. "Mobile information systems", 5 Febrer 2017, vol. 2017, núm. ID 6740585, p. 1-16. |
dc.identifier.issn | 1574-017X |
dc.identifier.uri | http://hdl.handle.net/2117/109446 |
dc.description.abstract | Planning future mobile networks entails multiple challenges due to the high complexity of the network to be managed. Beyond 4G and 5G networks are expected to be characterized by a high densification of nodes and heterogeneity of layers, applications, and Radio Access Technologies (RAT). In this context, a network planning tool capable of dealing with this complexity is highly convenient. The objective is to exploit the information produced by and already available in the network to properly deploy, configure, and optimise network nodes. This work presents such a smart network planning tool that exploits Machine Learning (ML) techniques. The proposed approach is able to predict the Quality of Service (QoS) experienced by the users based on the measurement history of the network. We select Physical Resource Block (PRB) per Megabit (Mb) as our main QoS indicator to optimise, since minimizing this metric allows offering the same service to users by consuming less resources, so, being more cost-effective. Two cases of study are considered in order to evaluate the performance of the proposed scheme, one to smartly plan the small cell deployment in a dense indoor scenario and a second one to timely face a detected fault in a macrocell network. |
dc.format.extent | 16 p. |
dc.language.iso | eng |
dc.publisher | HINDAWI |
dc.rights | Attribution 3.0 Spain |
dc.rights.uri | http://creativecommons.org/licenses/by/3.0/es/ |
dc.subject | Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Radiocomunicació i exploració electromagnètica |
dc.subject.lcsh | Machine learning |
dc.subject.lcsh | Mobile communication systems |
dc.subject.other | Machine learning |
dc.subject.other | Genetic algorithm |
dc.subject.other | Data analytics |
dc.subject.other | Quality of service |
dc.subject.other | Minimization of drive test |
dc.subject.other | Network planning |
dc.subject.other | Small cell deployment |
dc.subject.other | Cell outage compensation |
dc.title | A mobile network planning tool based on data analytics |
dc.type | Article |
dc.subject.lemac | Aprenentatge automàtic |
dc.subject.lemac | Comunicacions mòbils, Sistemes de |
dc.contributor.group | Universitat Politècnica de Catalunya. WiComTec - Grup de recerca en Tecnologies i Comunicacions Sense Fils |
dc.identifier.doi | 10.1155/2017/6740585 |
dc.description.peerreviewed | Peer Reviewed |
dc.relation.publisherversion | https://www.hindawi.com/journals/misy/2017/6740585/ |
dc.rights.access | Open Access |
local.identifier.drac | 21225179 |
dc.description.version | Postprint (published version) |
local.citation.author | Moysen, J.; Giupponi, L.; Mangues-Bafalluy, Josep |
local.citation.publicationName | Mobile information systems |
local.citation.volume | 2017 |
local.citation.number | ID 6740585 |
local.citation.startingPage | 1 |
local.citation.endingPage | 16 |
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