Vehicle-to-Everything Communication (V2X) technologies aim to meet strict quality-of-service (QoS) requirements of vehicular connectivity applications such as safety message exchange, remote driving, and sensor data sharing. The high reliability requirement is particularly important to enable safety relevant applications. Thus, predicting QoS levels becomes key to ensure the reliability of the connected vehicle applications. Recently, machine learning (ML) algorithms are demonstrated to provide dependable predictions to plan, simulate, and evaluate the performance of vehicular networks. In this paper, we propose ML aided New Radio (NR)-V2X QoS predictions scheme to provide Packet Delivery Ratio (PDR) and throughput predictions with the input of Modulation and Coding Schemes (MCS), distance-to-base station, Signal to Interference plus Noise Ratio (SINR), and packet size. Seven different ML algorithms based prediction models are trained and evaluated by using NR-V2X simulation data. We provide performance comparisons between Support Vector Regression (SVR), Deep Neural Network (DNN), Random Forest (RF), Gradient Boosting Machine (GBM), Extreme Gradient Boosting (XGBoost), Categorical Boosting (CatBoost), and Light GBM (LGBM) for predicting throughput and PDR. We demonstrate that CatBoost and RF are the best performing algorithms to predict throughput and PDR of NR-V2X networks, respectively.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Machine Learning Aided NR-V2X Quality of Service Predictions


    Beteiligte:
    Reyhanoglu, Aslihan (Autor:in) / Kar, Emrah (Autor:in) / Kumec, Feyzi Ege (Autor:in) / Kara, Yahya Sukur Can (Autor:in) / Karaagac, Sercan (Autor:in) / Turan, Bugra (Autor:in) / Coleri, Sinem (Autor:in)


    Erscheinungsdatum :

    26.04.2023


    Format / Umfang :

    208814 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Machine Learning Aided Low-Order Predictions of Fan Stage Broadband Interaction Noise

    Li, Nuo / Zhang, Yifan / Winkler, Julian et al. | AIAA | 2024


    Machine Learning Based Terminal Descent Predictions

    James Zach Wells / Krishna M Kalyanam | NTRS


    Computer-aided quality data analysis im technical service

    Seitschek,V. / Engel Maschinenbau,Schwertberg,DE | Kraftfahrwesen | 1987


    Enhanced EGNOS NOTAM Predictions Through Machine Learning Techniques

    Pérez, Román Rodríguez / Moreno, Victor Castro / García, Jorge Morán et al. | British Library Conference Proceedings | 2021