Modelo de rádio propagação em UHF para ambientes não homogêneos e climas distintos utilizando técnica de aprendizagem de máquina

Abstract

The digital TV broadcasts have greatly increased worldwide in recent years, especially in Brazil. The establishment and improvement of these transmission systems rely on models that take into account, among other factors, the geographical characteristics of the region, as these contribute to signal degradation. In Brazil, there is a great diversity of scenery and climates. For years several propagation models have been studied many for several frequency bands and types of paths. This thesis proposes an outdoor empirical radio propagation model for UHF band, which is used in digital TV. The proposed model estimates received power values can be applied to non-homogeneous paths and different climates, this latter presents innovative character for the UHF band. Different artificial intelligence techniques were chosen for theoretical and computational basis for having the ability to introduce, organize and describe quantitative and qualitative data quickly and efficiently, making it possible to determine the received power in a variety of settings and climates. The proposed model was applied to a city in the Amazon region with heterogeneous paths and wooded urban areas, fractions of freshwater among others. Measurement campaigns were conducted to obtain data signals from two digital TV stations in the metropolitan area of the city of Belém-Pará to model, compare and validate the model. The results are consistent. The model depicts a distinct difference between the two seasons of the studied year and small RMS errors for all cases studied. The validation of the model was based on the comparison with empirical and deterministic models.