Optimization of the propagation model choice by measuring field and artificial intelligence

Abstract

The propagation model to be chosen in the design of a digital terrestrial broadcast station is a tipping point for predicting the coverage area. There are several models, with specific characteristics that may be better than others in certain situations. This paper presents a study of the choice of propagation model, through the use of artificial intelligence (AI). A brief review of the most widely used propagation models in the literature, field measurements and simulations by the Progira coverage prediction software, which operates on the ArcGIS geoprocessing platform are presented. Using the propagation model criterion that presents the smallest error between the field measurement and the software simulation, an AI method of classification learning was developed. The objective of this method can choose, with the smallest error, the best propagation model in the entire study area, not restricted to the Sites measured in the field.