Enhanced wireless channel estimation through parametric optimization of hybrid ray launching-collaborative filtering technique
Fecha
2020Autor
Versión
Acceso abierto / Sarbide irekia
Tipo
Artículo / Artikulua
Versión
Versión publicada / Argitaratu den bertsioa
Impacto
|
10.1109/ACCESS.2020.2992033
Resumen
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 collabora ...
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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. [--]
Materias
Collaborative filtering,
3-D ray launching,
Pattern recognition,
Wireless channel
Editor
IEEE
Publicado en
IEEE Access, 2020, 8, 83070-83080
Departamento
Universidad Pública de Navarra. Departamento de Ingeniería Eléctrica, Electrónica y de Comunicación /
Nafarroako Unibertsitate Publikoa. Ingeniaritza Elektrikoa, Elektronikoa eta Telekomunikazio Ingeniaritza Saila /
Universidad Pública de Navarra/Nafarroako Unibertsitate Publikoa. Institute of Smart Cities - ISC
Versión del editor
Entidades Financiadoras
This 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.