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A novel approach for user equipment indoor/outdoor classification in mobile networks

2021-11-23Journal article Open access
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dc.contributor.authorAlves, Pedro
dc.contributor.authorSaraiva, Thaína
dc.contributor.authorBarandas, Marília
dc.contributor.authorDuarte, David
dc.contributor.authorMoreira, Dinis
dc.contributor.authorSantos, Ricardo
dc.contributor.authorLeonardo, Ricardo
dc.contributor.authorGamboa, Hugo
dc.contributor.authorVieira, Pedro
dc.date.accessioned2022-07-06T08:03:09Z
dc.date.available2022-07-06T08:03:09Z
dc.date.issued2021-11-23
dc.description.abstractThe ability to locate users and estimate traffic in mobile networks is still one of the major challenges when it comes to planning and optimizing the networks. Since indoor location is not always possible or precise, having the ability to distinguish indoor from outdoor traffic can be a valuable alternative and/or improvement. In this paper, two different machine learning algorithms are presented to classify a user’s environment, whether indoor or outdoor, using only data from a Long Term Evolution (LTE) network. To test both algorithms, two different measurement campaigns were done. Both campaigns used a smartphone to gather data from the user’s side. The first measurement campaign was done across 6 different cities, ranging from small rural areas to large urban environments, while the second was only done on a large urban city. On the second campaign, Network Traces (NT) data was also collected from the network side. The first algorithm consists on a Random Forest (RF) and the second relies on a Long Short Term Memory (LSTM), thus covering both more traditional machine learning and deep learning approaches. The results varied from 0.75 to 0.91 on the F1-Score, depending on the validation strategy, showing promising results.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationALVES, Pedro; [et al] – A novel approach for user equipment indoor/outdoor classification in mobile networks. IEEE Access. eISSN 2169-3536. Vol. 9 (2021), pp. 162671-162686.pt_PT
dc.identifier.doi10.1109/ACCESS.2021.3130429pt_PT
dc.identifier.eissn2169-3536
dc.identifier.urihttp://hdl.handle.net/10400.21/14783
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherIEEEpt_PT
dc.relationPOCI-01-0247-FEDER-033479pt_PT
dc.relation.publisherversionhttps://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9626002pt_PT
dc.subjectIndoor outdoor detectionpt_PT
dc.subjectMachine learning algorithmspt_PT
dc.subjectLong term evolutionpt_PT
dc.subjectMeasurement campaignspt_PT
dc.subjectSmartphonept_PT
dc.subjectNetwork tracespt_PT
dc.titleA novel approach for user equipment indoor/outdoor classification in mobile networkspt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.citation.endPage162686pt_PT
oaire.citation.startPage162671pt_PT
oaire.citation.titleIEEE Accesspt_PT
oaire.citation.volume9pt_PT
person.familyNameAlmeida
person.familyNameBarandas
person.familyNameMoreira
person.familyNameLeonardo
person.familyNameGamboa
person.familyNameVieira
person.givenNamePedro
person.givenNameMarília
person.givenNameDinis Benjamim Ferreira
person.givenNameRicardo
person.givenNameHugo
person.givenNamePedro
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person.identifier.scopus-author-id56436894500
person.identifier.scopus-author-id57200265948
person.identifier.scopus-author-id7004567421
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
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