Realistic Dataset for V2X Evaluation and Prediction

The integration of accurate and realistic wireless network simulations that reflect real-world scenarios is crucial for advancing research in intelligent communications. By utilizing accessible simulation tools and datasets, one can avoid the expensive and time-consuming processes of gathering datasets. This can significantly lower the barrier for research with the purpose of validating novel machine learning algorithms for the network. We present a user-friendly dataset based on the interconnection of the Sionna ray-tracer with the ns-3 discrete network simulator with realistic vehicular-mobility. This dataset is particularly suited for testing algorithms in dynamic vehicular networks, urban deployments, and adaptive communication systems. Our work lowers the entry barrier for experiments with wireless network data, fostering reproducible research in machine learning-based quality of service prediction, network optimization, and intelligent communication system design.