This paper presents a radio propagation model for the UHF band that is designed for an outdoor scenario in the Amazon region of Brazil and comprises city, water, and forest environments. The model is designed for the Mobile and Home Digital Television (M-DTV and H-DTV) services. In the case of M-DTV, the electric field is calculated at user height, while for H-DTV it considers a fixed antenna on houses roofs. The field calculation is based on Geometrical Optics (GO) and the Uniform Theory of Diffraction (UTD). The results for M-DTV show a good agreement with measurement data in an Amazonian city (Belém) for 521 MHz. Different parameters of the proposed model are analyzed: the transition zone city-water, the level of water, the incidence angle in the forest, and electrical parameters for forest. Finally, the comparison that was made between the electric fields for H-DTV and M-DTV shows a difference of up to 19 dB.

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This work presents and evaluates the use of geometric parameters of the environment in the prediction of the electric field in mixed city-river type environments, employing two techniques of Machine Learning (ML) as Artificial Neural Networks (ANN) and Neuro-Fuzzy System (NFS). For its development, measurements were carried out in Amazon Region, Belém city, in the 521 MHz band. The input parameters for an ANN and NFS are the distance between transmitter and receiver, the distance only over the river, the height of the ground, the radius of the first Fresnel ellipsoid, and the electric field of free space. The ANN is a Multilayer Perceptron Network (MLP) that uses the Levenberg-Marquardt training algorithm and cross-validation method. The NFS is an Adaptive Neuro-Fuzzy Inference System (ANFIS) that uses the model Sugeno. The results obtained compared with the classic literature models (ITU-R 1546 and Okumura-Hata) in the city for distances up 20 km and over the river for distances up 5 km. A quantitative analysis is performed between the measured and predicted data through the Standard deviation (SD), Root Mean Square Error (RMSE), and the Grey Relational Grade, combined with the Mean Absolute Percentage Error (GRG-MAPE). For ANN, the SD is 2.13, the RMSE is 2.11 dB, and the GRG-MAPE is 0.96. Also, for the NFS, the SD is 1.99, the RMSE is 2.06 dB, and the GRG-MAPE is 0.97. It should be noted that the transition zone between the city and the river was characterized by the proposed ANN and NFS in contrast with the classic literature models, which did not demonstrate coherence in the transition zone.INDEX TERMS Artificial neural networks, geometric parameters, neuro-fuzzy systems, mixed city-river path, radio propagation model.

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The present work describes the use of a simulation model based on asymptotic methods (ray tracing) on the propagation of ultra-wideband radio signals in a densely-arborized urban channel. The model was previously validated and adjusted using data obtained from measurement campaigns in the millimeter-wave band in locations different from the one to be analyzed here. The simulation uses deterministic methods to predict the received power, cross-polar discrimination, root mean square delay spread, and mean delay in a channel with a high density of scatterers (trees, buildings, and poles). Simulated signals were transmitted in the vertical and horizontal polarizations, considering non-specular reflections caused by rough surfaces and the effect of the transmitter's height variation in outdoor channels.