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

  • Datos identificativos

    Identificador: imarina:6389988
    Autores:
    Casino, FranLopez-Iturri, PeioAguirre, ErikAzpilicueta, LeyreFalcone, FranciscoSolanas, Agusti
    Resumen:
    © 2013 IEEE. In 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.
  • Otros:

    Autor según el artículo: Casino, Fran; Lopez-Iturri, Peio; Aguirre, Erik; Azpilicueta, Leyre; Falcone, Francisco; Solanas, Agusti
    Departamento: Enginyeria Informàtica i Matemàtiques
    Autor/es de la URV: Casino Cembellín, Francisco José / Solanas Gómez, Agustín
    Palabras clave: Wireless channel Pattern recognition Neural-network Model Framework Collaborative filtering 3-d ray launching
    Resumen: © 2013 IEEE. In 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.
    Áreas temáticas: Telecommunications Materials science (miscellaneous) Materials science (all) General materials science General engineering General computer science Engineering, electrical & electronic Engineering (miscellaneous) Engineering (all) Engenharias iv Engenharias iii Electrical and electronic engineering Computer science, information systems Computer science (miscellaneous) Computer science (all) Ciência da computação
    Acceso a la licencia de uso: https://creativecommons.org/licenses/by/3.0/es/
    ISSN: 2169-3536
    Direcció de correo del autor: franciscojose.casino@urv.cat agusti.solanas@urv.cat
    Identificador del autor: 0000-0003-4296-2876 0000-0002-4881-6215
    Fecha de alta del registro: 2024-10-12
    Versión del articulo depositado: info:eu-repo/semantics/publishedVersion
    Enlace a la fuente original: https://ieeexplore.ieee.org/document/9085406
    URL Documento de licencia: https://repositori.urv.cat/ca/proteccio-de-dades/
    Referencia al articulo segun fuente origial: Ieee Access. 8 83070-83080
    Referencia de l'ítem segons les normes APA: Casino, Fran; Lopez-Iturri, Peio; Aguirre, Erik; Azpilicueta, Leyre; Falcone, Francisco; Solanas, Agusti (2020). Enhanced wireless channel estimation through parametric optimization of hybrid ray launching-collaborative filtering technique. Ieee Access, 8(), 83070-83080. DOI: 10.1109/ACCESS.2020.2992033
    DOI del artículo: 10.1109/ACCESS.2020.2992033
    Entidad: Universitat Rovira i Virgili
    Año de publicación de la revista: 2020
    Tipo de publicación: Journal Publications
  • Palabras clave:

    Computer Science (Miscellaneous),Computer Science, Information Systems,Engineering (Miscellaneous),Engineering, Electrical & Electronic,Materials Science (Miscellaneous),Telecommunications
    Wireless channel
    Pattern recognition
    Neural-network
    Model
    Framework
    Collaborative filtering
    3-d ray launching
    Telecommunications
    Materials science (miscellaneous)
    Materials science (all)
    General materials science
    General engineering
    General computer science
    Engineering, electrical & electronic
    Engineering (miscellaneous)
    Engineering (all)
    Engenharias iv
    Engenharias iii
    Electrical and electronic engineering
    Computer science, information systems
    Computer science (miscellaneous)
    Computer science (all)
    Ciência da computação
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