Predicting Networks Before They Happen: Experimentation on a Real-Time V2X Digital Twin

TL;DR

This paper introduces a real-time V2X Digital Twin framework that combines live mobility data with deterministic channel simulation to predict network performance before physical events occur, validated in Tokyo with promising accuracy.

Contribution

It presents a novel integrated digital twin system for V2X networks that balances high-fidelity modeling with real-time constraints, enabling proactive network adaptation.

Findings

Achieved RSSI prediction with a maximum average error of 1.01 dB.

Forecasted LoS transitions within 250 ms latency.

Demonstrated feasibility of high-fidelity digital twins in urban environments.

Abstract

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.