International Journal of Antennas and Propagation · 2020 · 11 citations · 10 references
Empirical Hybrid AutoregressiveEngineeringAmazon Urbanized CitiesElectromagnetic PropagationRadio CommunicationGeophysical Signal ProcessingElectromagnetic CompatibilityMicrowave Device ModelingData ScienceMetropolitan AreaSystems EngineeringComputational ElectromagneticsStatisticsElectromagnetic WaveHybrid ArimaAntennaForecastingRadio PropagationEnergy PredictionSignal ProcessingIntelligent ForecastingCivil EngineeringArtificial Neural Network
This study sets out an empirical hybrid autoregressive integrated moving average (ARIMA) and artificial neural network (ANN) model designed to estimate electromagnetic wave propagation in densely forested urban areas. Received signal power intensity data was acquired through measurement campaigns carried out in the Metropolitan Area of Belém (MAB), in the Brazilian Amazon. Comparisons were made between estimates from classical least squares (LS) fitting and ITU (International Telecommunication Union) recommendation P. 1546-5. The results indicate the model is, at least, 44% more precise than every ITU estimate and, in some situations, is at least 11% better than an LS estimate, depending on the respective values of the relative error (RE).
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