Improving QoS Prediction in Urban V2X Networks by Leveraging Data from Leading Vehicles and Historical Trends

With the evolution of Vehicle-to-Everything (V2X) technology and increased deployment of 5G networks and edge computing, Predictive Quality of Service (PQoS) is seen as an enabler for resilient and adaptive V2X communication systems. PQoS incorporates data-driven techniques, such as Machine Learning (ML), to forecast/predict Key Performing Indicators (KPIs) such as throughput, latency, etc. In this paper, we aim to predict downlink throughput in an urban environment using the Berlin V2X cellular dataset. We select features from the ego and lead vehicles to train different ML models to help improve the predicted throughput for the ego vehicle. We identify these features based on an indepth exploratory data analysis. Results show an improvement in model performance when adding features from the lead vehicle. Moreover, we show that the improvement in model performance is model-agnostic.