Mobile cellular network-Based Positioning using Machine Learning

Authors

  • Reda Yagoub University of Ain Témouchent - Belhadj Bouchaib, Po Box 284, 46000, Algeria
  • Samia Bentaieb University of Ain Témouchent - Belhadj Bouchaib , Po Box 284, 46000, Algeria, Laboratory Signals and Images (LSI)
  • Roumaissa Anberi University of Ain Témouchent - Belhadj Bouchaib, Po Box 284, 46000, Algeria
  • Fouzia Bakhti University of Ain Témouchent - Belhadj Bouchaib, Po Box 284, 46000, Algeria

DOI:

https://doi.org/10.24425/ijet.2026.157920

Keywords

LTE, GSM, UMTS, Positioning, Machine Learning, Data preprocessing, Random Forest, Cell Id, localization

Abstract

Positioning systems are essential for various
applications, ranging from navigation to location-based services.
In the case of mobile cellular networks. This study explored the use
of mobile cellular network signals to develop a positioning system
for beehives. Our research introduces a new approach for
positioning using mobile cellular (LTE, UMTS, and GSM) radio
signal data. We trained a machine learning model to predict
geographic coordinates (latitude and longitude) based on various
parameters extracted from the intercepted radio signals. These
parameters include the Cell Identity, Mobile Country Code
(MCC), Mobile Network Code (MNC), Location Area Code (LAC),
and additional relevant identifiers.Our study offers a novel
approach for precise position determination by utilizing
information provided by mobile cellular signals.

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Published

2026-06-02

How to Cite

Yagoub, Reda, et al. “Mobile Cellular Network-Based Positioning Using Machine Learning”. International Journal of Electronics and Telecommunications, vol. 72, no. 2, June 2026, pp. 1-7, doi:10.24425/ijet.2026.157920.

Issue

Vol. 72 No. 2 (2026)

Section

Artykuły

License

Copyright (c) 2026 International Journal of Electronics and Telecommunications

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.


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