Autors: Gotseva N., Vlahov, A. G., Poulkov, V. K., Manolova, A. H.
Title: ML-Driven Prediction of QoS in C-V2X Scenarios
Keywords: C-V2X, LGBM, Machine Learning, QoS prediction, throughput prediction, vehicular communicationsAbstract: This paper explores the efficacy of a Light Gradient Boosting Machine (LGBM) model in predicting downlink throughput within a Cellular Vehicle-to-Everything (C-V2X) environment. Utilizing the Berlin V2X dataset, the model demonstrates high accuracy, achieving an R2 score of 97% and a mean absolute error (MAE) of approximately 3 Mbps. The study underscores the model's utility in enhancing vehicular communication systems by facilitating reliable quality-of-service (QoS) predictions. The model ensures efficient and effective throughput predictions by focusing on a minimal set of impactful network features and employing a simple supervised regression approach. References - T. Lohmar, A. Zaidi, H. Olofsson, et al., "Driving transformation in the automotive and road transport ecosystem with 5G, " Ericsson Technology Review, Sep. 2019.
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Issue
| 2024 59th International Scientific Conference on Information, Communication and Energy Systems and Technologies, ICEST 2024 - Proceedings, 2024, , https://doi.org/10.1109/ICEST62335.2024.10639683 |
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