Optimizing Propagation Models on Railway Communications Using Genetic Algorithms

Although the Okumura-Hata prediction model has been a widely used model to estimate radio network coverage, its application in railways environment requires calibration. The objective of this work is to present Genetic Algorithms as a solution in optimizing propagation models, proving that it can be used for optimizing the Okumura-Hata model on railway communications in order to improve its prediction of radio coverage.Several tests were carried out using different conditions allowing to establish the conditions that maximize the gain of the algorithm for this particular problem. The algorithm was applied to training samples and the resulting parameters were applied to different scenarios, showing improvements in the prediction results.