Publication
Improving QoS Prediction in Urban V2X Networks by Leveraging Data from Leading Vehicles and Historical Trends
Sanket Partani; Michael Zentarra; Anthony Kiggundu; Hans Dieter Schotten
In: IEEE 101st Vehicular Technology Conference. IEEE Vehicular Technology Conference (VTC-2025-Spring), June 17-20, Oslo, Norway, IEEE Xplore, 2025.
Abstract
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 en-
abler 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 in-
depth 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.