A digital twin framework with field calibrated propagation modeling for vehicular network applications

Digital Twin (DT) networks for connected vehicles
depend critically on accurate wireless communication models
that reflect real-world propagation conditions. This paper
presents a field-calibrated DT framework that integrates three-dimensional topography, physical vehicle telemetry, and standardized wireless propagation models to estimate path loss in vehicular network environments. The framework is validated through a case study at the Indian Institute of Information Technology, Sri City (IIITS), where a personal mobility vehicle equipped with sensors and a 4G LTE modem serves as the physical twin. Path loss estimation is performed using the 3GPP Urban Macro propagation model with log-normal shadowing, implemented within a Unity-based virtual environment constructed from OpenStreetMap data. Real-world drive tests along an identical campus route provide ground-truth measurements for calibration and validation. The calibrated model achieves a correlation coefficient of 0.756 and a Root Mean Square Error (RMSE) of 10.95 dB when compared against measured data. The validated framework demonstrates that integrating calibrated propagation models with cyber-physical DT architectures enables reliable radio coverage estimation for infrastructure planning in emerging vehicular and micro-mobility applications.