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    Design of a Non-deterministic Model for a Propagation of VHF Radio Signal in the Ecuadorian Jungle

    Autor: 
    Rodas, Federico
    ;
    Paredes-Calderón, Manolo
    ;
    Giménez de Ory, Elena (1)
    ;
    Garzón, Diego
    ;
    Ayala, Emilia
    Fecha: 
    2020
    Palabra clave: 
    mathematical model; longley rice; propagation model; Scopus(2)
    Tipo de Ítem: 
    conferenceObject
    URI: 
    https://reunir.unir.net/handle/123456789/10272
    DOI: 
    https://doi.org/10.1007/978-3-030-42531-9_8
    Dirección web: 
    https://link.springer.com/chapter/10.1007/978-3-030-42531-9_8#citeas
    Resumen:
    This paper develops a propagation model of electromagnetic signals emitted at frequencies of 20 and 40 MHz for the Ecuadorian jungle. The expected results of this research will be applied to produce a complete coverage map for wireless communications technologies, which will optimize the radio spectrum in operation carried out by the Armed Forces in Ecuadorian border jungle. The final expression found is an adjustment function that relates the Receiving Power (PRX) to factors that determine the geometry of the Fresnell Zone (Conectivity). The resulting model of the research improves the discrepancy between the simulated power (PRL) in commercial software and a sample of measured wireless transmissions in situ. The analysis was based on the results and methodology presented by Longley-Rice. It was determined the non-normality of the discrepancy between the losses (LlR) calculated by Longley Rice Model (LMR) and the data obtained in the field. To solve this point, correction coefficients were added on the expression of LMR. Subsequently, the mathematical expression was linearized to implement multivariate linear adjustment techniques. Alternative formulations to the Linear Regression model were sought and their goodness of fit was compared; all these techniques are introduced theatrically. To conclude, the study includes a Kolmogorov Test to grant normality and goodness of fit. Mathematical modelling software such as Matlab, R and SPSS were used for the formulation and numerical analysis. Finally, we found a model that have a R2 of 99.995%.
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