Roberto Pegurri

Roberto Pegurri, Diego Gasco, Francesco Linsalata et al.

This paper presents VaN3Twin-the first open-source, full-stack Network Digital Twin (NDT) framework for simulating the coexistence of multiple Vehicle-to-Everything (V2X) communication technologies with accurate physical-layer modeling via ray tracing. VaN3Twin extends the ms-van3t simulator by integrating Sionna Ray Tracer (RT) in the loop, enabling high-fidelity representation of wireless propagation, including diverse Line-of-Sight (LoS) conditions with focus on LoS blockage due to other vehicles' meshes, Doppler effect, and site-dependent effects-e.g., scattering and diffraction. Unlike conventional simulation tools, the proposed framework supports realistic coexistence analysis across DSRC and C-V2X technologies operating over shared spectrum. A dedicated interference tracking module captures cross-technology interference at the time-frequency resource block level and enhances signal-to-interference-plus-noise ratio (SINR) estimation by eliminating artifacts such as the bimodal behavior induced by separate LoS/NLoS propagation models. Compared to field measurements, VaN3Twin reduces application-layer disagreement by 50% in rural and over 70% in urban environments with respect to current state-of-the-art simulation tools, demonstrating its value for scalable and accurate digital twin-based V2X coexistence simulation.

Roberto Pegurri, Habu Shintaro, Francesco Linsalata et al.

Emerging safety-critical Vehicle-to-Everything (V2X) applications require networks to proactively adapt to rapid environmental changes rather than merely reacting to them. While Network Digital Twins (NDTs) offer a pathway to such predictive capabilities, existing solutions typically struggle to reconcile high-fidelity physical modeling with strict real-time constraints. This paper presents a novel, end-to-end real-time V2X Digital Twin framework that integrates live mobility tracking with deterministic channel simulation. By coupling the Tokyo Mobility Digital Twin-which provides live sensing and trajectory forecasting-with VaN3Twin-a full-stack simulator with ray tracing-we enable the prediction of network performance before physical events occur. We validate this approach through an experimental proof-of-concept deployed in Tokyo, Japan, featuring connected vehicles operating on 60 GHz links. Our results demonstrate the system's ability to predict Received Signal Strength (RSSI) with a maximum average error of 1.01 dB and reliably forecast Line-of-Sight (LoS) transitions within a maximum average end-to-end system latency of 250 ms, depending on the ray tracing level of detail. Furthermore, we quantify the fundamental trade-offs between digital model fidelity, computational latency, and trajectory prediction horizons, proving that high-fidelity and predictive digital twins are feasible in real-world urban environments.