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is study explores the use of a hybrid Autoregressive Integrated Moving Average (ARIMA) and Neural Network modelling for estimates of the electric field along vertical paths (buildings) close to Digital Television (DTV) transmitters. e work was carried out in Belém city, one of the most urbanized cities in the Brazilian Amazon and includes a case study of the application of this modelling within the subscenarios found in Belém. Its results were compared with the ITU recommendations P. 1546-5 and proved to be better in every subscenario analysed. In the worst case, the estimate of the model was approximately 65% better than that of the ITU. We also compared this modelling with a classic modelling technique: the Least Squares (LS) method. In most situations, the hybrid model achieved better results than the LS.

This paper conducts a simulation analysis of probability reception in detecting radio signal levels in an indoor environment. Femtocell simulations were performed that considered fourth generation (4G) networks, operating at 2.6 GHz (LTE-4G), and WiMAX, at 3.5 GHz. Two indoor scenarios were chosen for this study and simulated signals with and without and interference. The Padè approximant model was used to calculate the propagation loss and received power probability. Environmental factors such as the number of walls/floors are taken into account in the simulation. Finally, a measurement campaign was carried out to validate the simulated results.

is 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. e 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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