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TITLE:
Enhanced wireless channel estimation through parametric optimization of hybrid ray launching-collaborative filtering technique - imarina:6389988

URV's Author/s:Casino Cembellín, Francisco José / Solanas Gómez, Agustín
Author, as appears in the article.:Casino, Fran; Lopez-Iturri, Peio; Aguirre, Erik; Azpilicueta, Leyre; Falcone, Francisco; Solanas, Agusti
Author's mail:franciscojose.casino@urv.cat
agusti.solanas@urv.cat
Author identifier:0000-0003-4296-2876
0000-0002-4881-6215
Journal publication year:2020
Publication Type:Journal Publications
ISSN:2169-3536
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
Papper original source:Ieee Access. 8 83070-83080
Abstract:© 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.
Article's DOI:10.1109/ACCESS.2020.2992033
Link to the original source:https://ieeexplore.ieee.org/document/9085406
Papper version:info:eu-repo/semantics/publishedVersion
licence for use:https://creativecommons.org/licenses/by/3.0/es/
Department:Enginyeria Informàtica i Matemàtiques
Licence document URL:https://repositori.urv.cat/ca/proteccio-de-dades/
Thematic Areas: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
Keywords:Wireless channel
Pattern recognition
Neural-network
Model
Framework
Collaborative filtering
3-d ray launching
Entity:Universitat Rovira i Virgili
Record's date:2024-10-12
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