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

2026-01-23

AI Summary

This work addresses the challenge of enabling proactive adaptation in V2X networks under stringent real-time constraints, where existing digital twin approaches struggle to simultaneously achieve high fidelity and low latency. The paper presents the first end-to-end real-time digital twin framework for V2X, integrating real-time traffic trajectory prediction with deterministic ray-tracing-based channel simulation (built upon VaN3Twin). This framework enables, for the first time, high-fidelity and low-latency performance prediction of 60 GHz links in real-world urban environments. Experimental results demonstrate that the predicted RSSI exhibits an average maximum error of only 1.0 dB, while end-to-end latency for line-of-sight (LoS) state transitions remains below 250 ms. Furthermore, the study quantifies the inherent trade-offs among model fidelity, computational latency, and prediction time horizon.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Application Domains: Mobility, Driving & Flight
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Virtualization and resource management in Web systems and infrastructures
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Social Networks and Social Media: Influence propagation, information diffusion, and the prediction on networks

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.

Problem

• V2X
• Network Digital Twin
• real-time prediction
• channel simulation
• mobility forecasting

Innovation

• V2X Digital Twin
• real-time prediction
• ray tracing
• mobility forecasting
• network performance prediction