Radiofrequency Signal Levels Predition Using Machine Learning Models

Abstract

Mobile communication is rapidly evolving, generating a demand for networks with high quality and greater capacity. For efficient and reliable network design, it is essential to use accurate reception level prediction models to determine radio network coverage and prevent interference. Traditional models based on path loss propagation have limited accuracy, when used in urban environments, and, especially, when considering the devices' mobility. The main objective of this work is to propose a viable alternative to improve this accuracy with the use of machine learning techniques. The tests carried out in this work show that the use of the random forest technique together with attributes such as: geographic coordinates, distance, azimuth and antenna gain presents good results.