Mobile Network Analysis for Heatmap Prediction Using Machine Learning Techniques

• Evelim Bacury Rocha UFAM
• Márcio Éric Lamêgo Valente UFAM
• Vandermi João Da Silva UFAM

DOI: https://doi.org/10.5753/connect.2025.12112

Abstract

This project aims to use Machine Learning techniques to create heatmaps that represent signal quality in 5G and 4G LTE networks, especially in indoor and densely urban environments, contributing to the optimization of mobile networks. The goal is to generate heatmaps that help identify areas in need of improved signal coverage, particularly in complex environments. So far, signal quality data has been collected in both indoor and outdoor environments in the state of Amazonas. These data have undergone preprocessing for noise removal and handling of missing values. A prototype has been developed for visualizing the heatmaps, allowing for signal quality analysis across different locations. The next steps include applying advanced data mining techniques and building more robust predictive models, along with integrating new features into the prototype.

References

Afroz, F., Subramanian, R., Heidary, R., Sandrasegaran, K., and Ahmed, S. (2015). Sinr, rsrp, rssi and rsrq measurements in long term evolution networks. International Journal of Wireless & Mobile Networks.

Fayyad, U. M., Piatetsky-Shapiro, G., Smyth, P., and Uthurusamy, R. (1996). Advances in knowledge discovery and data mining. American Association for Artificial Intelligence.

Haskić, H. and Radončić, A. (2024). The effects of 5g network on people and the environment: A machine learning approach to the comprehensive analysis. World Journal of Advanced Engineering Technology and Sciences, 11(1):301–309.

Kaur, J., Khan, M. A., Iftikhar, M., Imran, M., and Haq, Q. E. U. (2021). Machine learning techniques for 5g and beyond. IEEE Access, 9:23472–23488.

Mogyorósi, F., Revisnyei, P., Pašić, A., Papp, Z., Törös, I., Varga, P., and Pašić, A. (2022). Positioning in 5g and 6g networks—a survey. Sensors, 22(13):4757.

Morocho-Cayamcela, M. E., Lee, H., and Lim, W. (2019). Machine learning for 5g/b5g mobile and wireless communications: Potential, limitations, and future directions. IEEE access, 7:137184–137206.

Saari, O. (2022). Heatmap reporting tool: System testing.

Sarkar, S. and Debnath, A. (2021). Machine learning for 5g and beyond: Applications and future directions. In 2021 Second International Conference on Electronics and Sustainable Communication Systems (ICESC), pages 1688–1693. IEEE.

Zhang, C., Patras, P., and Haddadi, H. (2019). Deep learning in mobile and wireless networking: A survey. IEEE Communications surveys & tutorials, 21(3):2224–2287.