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dc.creatorCasino, Franes_ES
dc.creatorLópez Iturri, Peioes_ES
dc.creatorAguirre Gallego, Erikes_ES
dc.creatorAzpilicueta Fernández de las Heras, Leyrees_ES
dc.creatorFalcone Lanas, Francisco Javieres_ES
dc.creatorSolanas, Agusties_ES
dc.date.accessioned2020-07-03T12:54:10Z
dc.date.available2020-07-03T12:54:10Z
dc.date.issued2020
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.issn2169-3536
dc.identifier.urihttps://hdl.handle.net/2454/37295
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.language.isoengen
dc.publisherIEEEen
dc.relation.ispartofIEEE Access, 2020, 8, 83070-83080en
dc.rightsThis work is licensed under a Creative Commons Attribution 4.0 License.en
dc.subjectCollaborative filteringen
dc.subject3-D 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/articleen
dc.typeArtículo / Artikuluaes
dc.contributor.departmentUniversidad Pública de Navarra. Departamento de Ingeniería Eléctrica, Electrónica y de Comunicaciónes_ES
dc.contributor.departmentNafarroako Unibertsitate Publikoa. Ingeniaritza Elektriko, Elektroniko eta Telekomunikazio Sailaeu
dc.contributor.departmentUniversidad Pública de Navarra / Nafarroako Unibertsitate Publikoa. ISC - Institute of Smart Citieses_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccessen
dc.rights.accessRightsAcceso abierto / Sarbide irekiaes
dc.identifier.doi10.1109/ACCESS.2020.2992033
dc.relation.projectIDinfo:eu-repo/grantAgreement/EC/H2020/832735en
dc.relation.publisherversionhttps://doi.org/10.1109/ACCESS.2020.2992033
dc.type.versioninfo:eu-repo/semantics/publishedVersionen
dc.type.versionVersión publicada / Argitaratu den bertsioaes


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