Utilização de inteligência computacional no aprimoramento dos modelos de propagação perda no espaço livre, Okumura-Hata, COST 231, ECC-33, Egli e a recomendação ITU-P.1546

Abstract

This research proposes the enhancement of electromagnetic wave propagation loss prediction models through the integration of artificial neural networks, genetic algorithms, and differential evolution. Traditional models, such as Free Space Loss, Okumura-Hata, Cost 231, ECC-33, Egli, and the ITU-P.1546 Recommendation, are combined with these methods to predict losses, enabling the generation of analytical expressions for their calculation. In this context, the proposed algorithms are classified as hybrid models, standing out by contrasting traditional approaches. A significant contribution of this study is the inclusion of altitude as an additional parameter in the analytical expressions, improving accuracy in path loss calculation through the developed prediction models.