Proactive Quality of Service Mapping in 6G Vehicular Networks: A Data-Driven Spatial Approach Using Random Forest Algorithm

Abstract

Proactive Quality of Service (QoS) estimation is crucial for reliable, low-latency 6G vehicular networks. This paper proposes a data-driven framework for generating predictive QoS heatmaps, significantly reducing the overhead of continuous channel state monitoring. Leveraging an OMNeT++/Simu5G and Simulation of Urban Mobility cosimulation, a high-fidelity dataset of urban spatiotemporal network dynamics is generated. A Random Forest regressor is trained using only two-dimensional vehicular geographical coordinates to predict a spatially aggregated QoS score, explicitly integrating normalized Signal-to-Interference-plus-Noise Ratio, Throughput, Medium Access Control delay, and Packet Loss Rate. Experimental evaluation compares Random Forest against standard regression baselines, showing that the model provides a competitive trade-off between prediction accuracy, non-linear spatial modeling capability, and computational efficiency. Ultimately, this predictive topography serves as a foundational knowledge base for 6G Digital Twins, enabling Deep Reinforcement Learning based network-aware vehicular routing.