ORIGINAL RESEARCH article
Front. Artif. Intell.
Sec. Machine Learning and Artificial Intelligence
Volume 8 - 2025 | doi: 10.3389/frai.2025.1597981
This article is part of the Research TopicAdvances in Edge Intelligent Connectivity Technologies for Autonomous Vehicles ApplicationsView all 3 articles
Machine Learning for Improved Path Loss Prediction in Urban Vehicle-to-Infrastructure Communication Systems
Provisionally accepted- 1ENISO, NNOCCS Laboratory, University of Sousse, Sousse, Tunisia
- 2Higher National Engineering School of Tunis, Tunis, Tunisia
- 3National School of Engineering Sousse - ENISo, Sousse, Tunisia
- 4Dept. of Electrical and Computer Engineering, International Islamic University Malaysia, Kuala Lumpur, Malaysia
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Path loss prediction is crucial to facilitate reliable vehicle-to-infrastructure (V2I) communications. In this study, machine learning techniques are investigated for path loss modeling using empirical measurements at 5.9 GHz from eight Road Side Unit (RSU) sites. The performance of Extreme Gradient Boosting (XGBoost) and Multilayer Perceptron (MLP) models is contrasted with traditional empirical models such as the Dual Slope and 3rd Generation Partnership Project (3GPP) models in three varied urban environments: open, suburban, and densely urbanized cities. The findings indicate that machine learning models, in particular XGBoost, consistently outperform traditional models with the lowest Root Mean Square Error (RMSE) in complicated urban environments. For additional robustness in prediction, we propose an innovative environmental classification system based on building density, street geometry, and transmitter position. Feature importance examination reveals that distance, environmental class, and transmitter height are the most significant factors affecting path loss prediction accuracy. These observations aid the development of adaptive V2I communication systems and provide valuable guidelines for enhancing reliability in diverse urban environments.
Keywords: Path loss modeling, vehicle-to-infrastructure (V2I), Path loss prediction, Machine Learning (ML), XGBoost, multilayer perceptron (MLP), 3GPP Model, Dual slope model
Received: 22 Mar 2025; Accepted: 06 Jun 2025.
Copyright: © 2025 Mongi, Jalel, Mohamed Hadi and Jamel. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: BEN AMEUR Mongi, ENISO, NNOCCS Laboratory, University of Sousse, Sousse, Tunisia
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