Proposition for Path Loss Prediction Models with the Inclusion of Altitude Parameter and Optimization Through Evolutionary Algorithms

Abstract In this paper, it is presented a general optimization methodology for improving empirical models predicting Okumura-Hata, Cost-231, ECC-33, and Egli using Genetic and Differential Evolution Algorithms. The methodology was tested using georeferenced signal power samples from Uberlândia, Brazil, for a television channel operating at 569.142857 MHz. Each parameter of the model formulas was adjusted with a variable optimized by the algorithms. A significant innovation was the inclusion of an altitude parameter weighted by an optimized coefficient, which notably enhanced the prediction accuracy. The primary contribution of this work is the development of a set of analytical equations derived from the proposed methodology, eliminating the need for computational power to estimate path loss for the evaluated models in the area in question. The performance of these equations was assessed using the Mean Squared Error (MSE) metric, demonstrating improvements of up to 92.03% over standard models, contingent on the empirical model and optimization algorithm applied.