Radiofrequency Signal Levels Predition Using Machine Learning Models



Radiofrequency signal levels, machine learning, path loss, mobile comunication


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.


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Author Biographies

André Luiz Barcellos, nstituto Militar de Engenharia (IME) Urca, Rio de Janeiro-RJ, zip code: 22.290-270, Brazil.

He has a degree in Electrical Engineering (emphasis on Telecommunications) from University Estacio de Sá, in 2006, He is currently a graduate research student with the Instituto Militar de Engenharia (IME). He has experience in multidisciplinary areas, with an emphasis on telecommunications, data science, low power electric and propagation research.

Julio Cesar Duarte, nstituto Militar de Engenharia (IME) Urca, Rio de Janeiro-RJ, zip code: 22.290-270, Brazil.

He has a degree in Computer Engineering from Instituto Militar de Engenharia (IME), in 1998, a Master's in Informatics from Pontificia Universidade Catolica do Rio de Janeiro (PUC-Rio), in 2003, and a Ph.D. in Informatics from PUC-Rio, in 2009. He is currently a professor at the Graduate Program in Systems and Computing at IME. He has experience in multidisciplinary areas, with an emphasis on systems development, working mainly on the following topics: machine learning, deep learning, artificial intelligence, Portuguese natural language processing and malware analysis.

André Chaves Mendes, Instituto Militar de Engenharia (IME) Urca, Rio de Janeiro-RJ, zip code: 22.290-270, Brazil.

He has graduated since 2001 in Electrical Engineering (emphasis on Telecommunications) from the Polytechnic School of the University of São Paulo (Poli-USP), Brazil. He then obtained his Master's degree, in 2010, and D.Sc., in 2015, both in Electrotechnical Engineering from the Federal University of Rio de Janeiro (COPPE/UFRJ), Brazil. He is also part of the Postgraduate Program in Systems Engineering and Computer Science (PPGSC) of the Instituto Militar de Engenharia (IME) (IME), based in Rio de Janeiro, Brazil, since 2017, performing as a collaborating professor. He is currently a post-doctoral researcher at the Department of Autonomous Systems Engineering, University of Vigo, Spain. His main topics of interest are Industrial Physical-Cybernetic Systems, Internet of Things applied to Industry (IIoT), Intelligent Systems and Data Analytics.


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How to Cite

Barcellos, A. L., Duarte, J. C., & Mendes, A. C. (2022). Radiofrequency Signal Levels Predition Using Machine Learning Models. IEEE Latin America Transactions, 21(2), 351–357. Retrieved from