Enhanced wireless channel estimation through parametric optimization of hybrid ray launching-collaborative filtering technique

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dc.contributor.authorCasino, Fran
dc.contributor.authorLópez Iturri, Peio
dc.contributor.authorAguirre Gallego, Erik
dc.contributor.authorAzpilicueta Fernández de las Heras, Leyre
dc.contributor.authorFalcone Lanas, Francisco
dc.contributor.authorSolanas, Agustí
dc.contributor.departmentIngeniaritza Elektrikoa, Elektronikoaren eta Telekomunikazio Ingeniaritzareneu
dc.contributor.departmentInstitute of Smart Cities - ISCen
dc.contributor.departmentIngeniería Eléctrica, Electrónica y de Comunicaciónes_ES
dc.date.accessioned2020-07-03T12:54:10Z
dc.date.available2020-07-03T12:54:10Z
dc.date.issued2020
dc.description.abstractIn this paper, an enhancement of a hybrid simulation technique based on combining collaborative filtering with deterministic 3D ray launching algorithm is proposed. Our approach implements a new methodology of data depuration from low definition simulations to reduce noisy simulation cells. This is achieved by processing the maximum number of permitted reflections, applying memory based collaborative filtering, using a nearest neighbors' approach. The depuration of the low definition ray launching simulation results consists on discarding the estimated values of the cells reached by a number of rays lower than a set value. Discarded cell values are considered noise due to the high error that they provide comparing them to high definition ray launching simulation results. Thus, applying the collaborative filtering technique both to empty and noisy cells, the overall accuracy of the proposed methodology is improved. Specifically, the size of the data collected from the scenarios was reduced by more than 40% after identifying and extracting noisy/erroneous values. In addition, despite the reduced amount of training samples, the new methodology provides an accuracy gain above 8% when applied to the real-world scenario under test, compared with the original approach. Therefore, the proposed methodology provides more precise results from a low definition dataset, increasing accuracy while exhibiting lower complexity in terms of computation and data storage. The enhanced hybrid method enables the analysis of larger complex scenarios with high transceiver density, providing coverage/capacity estimations in the design of heterogeneous IoT network applications.en
dc.description.sponsorshipThis work was supported in part by the European Commission LOCARD Project under Grant 832735 and under Project RTI2018-095499-B-C32 and Project RTI2018-095499-B-C31, and in part by the Ministerio de Ciencia, Innovación y Universidades, Gobierno de España (MCIU/AEI/FEDER, UE). The work of Agusti Solanas was supported in part by the Government of Catalonia (GC) under Grant 2017-DI-002 and Grant 2017-SGR-896, and in part by the Fundació PuntCAT with the Vinton Cerf Distinction.en
dc.format.extent11 p.
dc.format.mimetypeapplication/pdfen
dc.identifier.citationF. Casino, P. Lopez-Iturri, E. Aguirre, L. Azpilicueta, F. Falcone and A. Solanas, 'Enhanced Wireless Channel Estimation Through Parametric Optimization of Hybrid Ray Launching-Collaborative Filtering Technique,' in IEEE Access, vol. 8, pp. 83070-83080, 2020, doi: 10.1109/ACCESS.2020.2992033.en
dc.identifier.doi10.1109/ACCESS.2020.2992033
dc.identifier.issn2169-3536
dc.identifier.urihttps://academica-e.unavarra.es/handle/2454/37295
dc.language.isoengen
dc.publisherIEEEen
dc.relation.ispartofIEEE Access, 2020, 8, 83070-83080en
dc.relation.projectIDinfo:eu-repo/grantAgreement/European Commission/Horizon 2020 Framework Programme/832735/
dc.relation.publisherversionhttps://doi.org/10.1109/ACCESS.2020.2992033
dc.rightsThis work is licensed under a Creative Commons Attribution 4.0 License.en
dc.rights.accessRightsinfo:eu-repo/semantics/openAccess
dc.subjectCollaborative filteringen
dc.subject3D ray launchingen
dc.subjectPattern recognitionen
dc.subjectWireless channelen
dc.titleEnhanced wireless channel estimation through parametric optimization of hybrid ray launching-collaborative filtering techniqueen
dc.typeinfo:eu-repo/semantics/article
dc.type.versioninfo:eu-repo/semantics/publishedVersion
dspace.entity.typePublication
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