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 communications

Abstract: 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.

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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

Copyright IEEE

Цитирания (Citation/s):
1. Tezcan M.G., Yazici I., A Novel Framework for QoS Prediction of V2X in 5G and B5G Networks: a Unified Approach with Explainable Artificial Intelligence (XAI) and Nested Cross-Validation, 2025, 2025 7th International Conference on Smart Applications Communications and Networking Smartnets 2025, issue 0, DOI 10.1109/SmartNets65254.2025.11106841 - 2025 - в издания, индексирани в Scopus и/или Web of Science
2. Partani S., Zentarra M., Kiggundu A., Schotten H.D., Improving QoS Prediction in Urban V2X Networks by Leveraging Data from Leading Vehicles and Historical Trends, 2025, IEEE Vehicular Technology Conference, issue 0, DOI 10.1109/VTC2025-Spring65109.2025.11174805, issn 15502252 - 2025 - в издания, индексирани в Scopus и/или Web of Science
3. Mishra G., Rath H.K., Yarlagadda S.B., Stationarity-Aware QoS Prediction for Platooning in V2X Networks, 2026, IEEE International Conference on Pervasive Computing and Communications Workshops Percom Workshops, issue 2026, DOI 10.1109/PerComWorkshops68308.2026.11585407, issn 28365348, eissn 27668576 - 2026 - в издания, индексирани в Scopus
4. Karakas A.S., Ozcan A.V., Canberk B., Mobility-Aware Deep Sequential Learning for QoS Forecasting in Radio Access Networks, 2026, 2026 IEEE International Mediterranean Conference on Communications and Networking Meditcom 2026, issue 0, DOI 10.1109/MeditCom67211.2026.11641011 - 2026 - в издания, индексирани в Scopus

Вид: публикация в международен форум, публикация в реферирано издание, индексирана в Scopus и Web of Science