V2X Digital Twin Framework for Advanced Simulation

Updated 30 January 2026

V2X Digital Twin Frameworks are advanced cyber-physical architectures that combine mobility data, high-fidelity ray tracing, and protocol emulation to mirror real-world vehicular networks.
They employ a layered design integrating scenario management, physical-layer fidelity, and multi-technology coexistence to achieve accurate and scalable network simulations.
The framework supports predictive network management, proactive routing, and robust cybersecurity measures for next-generation intelligent transportation systems.

A Vehicle-to-Everything (V2X) Digital Twin Framework is an advanced cyber-physical architecture that enables high-fidelity, predictive, and cross-technology modeling of vehicular communications. Such frameworks synthesize real-time mobility data, environmental representations, channel simulation—including sophisticated ray tracing—and protocol stack emulation to mirror, anticipate, and virtually orchestrate the operation of highly dynamic V2X networks across heterogeneous radio access technologies (RATs). The transition from stochastic channel abstractions to deterministic, ray-traced models—epitomized by the VaN3Twin framework—marks a paradigm shift in reliable, scalable V2X simulation and validation (Pegurri et al., 20 May 2025).

1. Layered Architectural Design and Real-Time Data Integration

V2X Digital Twin architectures typically adopt a full-stack, modular layer design:

Scenario & Mobility Management: Real-time vehicle states (positions $p_n(t)$ , velocities $v_n(t)$ , headings $\theta_n(t)$ ) are generated using traffic simulators (e.g., SUMO, CARLA) or imported as ground-truth traces. A dedicated Vehicle Data Provider (VDP) module disseminates these states via UDP, supporting both high-throughput feeding to upper simulation layers and low-latency interfacing with ray tracing engines.
Ray-Tracing Propagation Layer: Physical-layer propagation is modeled by integrating GPU-accelerated ray tracing (e.g., Sionna @@@@10@@@@), which constructs and maintains a 3D digital twin encompassing static infrastructure (buildings, trees) and dynamic entities (vehicles, pedestrians). For each Tx–Rx event, the ray tracer computes the admissible path set $\mathcal{R}_{i,j} = \{ r_k \}$ , capturing LoS/NLoS transitions from vehicle blockage, multipath components from reflection, scattering, diffraction, and per-path Doppler shifts due to mobility.
PHY/MAC Emulation: Swappable ns-3 protocol stack models for DSRC (IEEE 802.11p), LTE-V2X, and NR-V2X override native stochastic channels, querying the ray tracer for deterministic channel parameters per packet transaction. This interleaved architecture ensures channel realism at every transmission, facilitating accurate SINR and packet error calculations.
Application Layer: Implements ETSI C-ITS CAM/CPM services, leveraging full protocol stack depth (RRC, PDCP, RLC, MAC, PHY) to support workload-relevant, standards-compliant V2X messaging.

Environmental state synchronization is maintained by streaming vector-map data (e.g., OpenStreetMap meshes) and dynamic mobility updates at runtime, repositioning vehicle meshes in the ray tracer only above a movement threshold $\Delta d_{\min} = \max(\Delta d_0, c \cdot v \cdot \tau_{coh})$ to balance fidelity and computational efficiency (Pegurri et al., 20 May 2025).

2. Embedded Ray-Tracing: Computational and Physical Layer Fidelity

Ray-traced channel models supplant stochastic models with deterministic, site-specific propagation calculations. On each packet event:

PHY triggers a channel request for Tx–Rx pair $(v_i, v_j)$ .
RT engine computes $\mathcal{R}_{i,j}$ $R_{i, j}$ up to $K$ $K$ bounces, outputting:
- Total Path Gain: $G_{i,j} = |\sum_{k=1}^K \alpha_k|^2$
- First-Tap Delay: $\tau_{i,j} = \min_k \tau_k$
- LoS Indicator: $\lambda_{i,j} = 1$ iff any $r_k$ is LoS
Channel impulse responses:

$h_{i,j}(t) = \sum_{k=1}^K \alpha_k e^{j2\pi\phi_k t} a_T(\psi_k) a_R^T(\theta_k) g(t-\tau_k)$

Physical-layer metrics:
- Path Loss: $PL_{i,j} = -10 \log_{10} G_{i,j}$
- RMS Delay Spread:
$\sigma_\tau = \sqrt{\left(\sum_k |\alpha_k|^2 \tau_k^2 / \sum_k |\alpha_k|^2 \right) - \left(\sum_k |\alpha_k|^2 \tau_k / \sum_k |\alpha_k|^2 \right)^2}$ - Angular Spreads: $\sigma_\psi$ , $\sigma_\theta$ - Doppler per path: $\phi_k = (v_{rel} \cdot u_k)/\lambda_0$

This framework captures high-dimensional propagation characteristics, including cross-vehicle blockage, localized scatter, and dynamic multipath, enabling rigorous site-dependent performance evaluation (Pegurri et al., 20 May 2025).

3. Multi-Technology Coexistence and Interference Analysis

To realistically simulate coexistence on shared V2X spectrum (5.9 GHz), VaN3Twin defines a global OFDM resource grid $Z = F \times S$ partitioned by active RATs: DSRC, LTE-V2X, NR-V2X. Each transmitter entity is tracked for its technology type, transmit bandwidth, and PSD profile.

The interference tracking module decomposes packet reception events into resource blocks $z \in Z$ , computing cross-technology SINR as:

$SINR_{w,z} = \frac{P_{Rx, w, z}}{N_{0, z} + \sum_{w' \neq w} P_{Rx, w', z}}$

The fine-grained resource block modeling smooths SINR distributions and eliminates artificial LoS/NLoS bimodalities that characterize conventional models. The per-technology SINR is bandwidth-weighted across resource blocks, enabling accurate perception of partial spectral overlap and dynamic interference (Pegurri et al., 20 May 2025).

4. Validation Methodology and Performance Metrics

Field validation campaigns (urban and rural) with GNSS–RTK ground-truth and OBU packet logging establish quantitative agreement with real-world measurements. Metrics include:

Propagation-level RSSI agreement: VaN3Twin closely matches empirical 1/ $d^2$ trends and unimodal urban RSSI spread, while stochastic models exhibit unrealistic smoothing and bimodal artifacts.
Application-level disagreement ratio ( $DR = \frac{|M \Delta R|}{|M \cup R|}$ ):

Scenario	DR (ms-van3t)	DR (VaN3Twin)	Reduction
Rural	12.8%	6.5%	-49.3%
Urban	27.9%	8.2%	-70.8%

Coexistence simulation: 20 vehicles (10 DSRC / 10 NR-V2X, 10 MHz):
- VaN3Twin yields single-peaked SINR PDFs; legacy models yield bimodal overshoot.
- Disagreement in interferer detection: 21.7% overall, 34.7% in strong NLoS blockage.
- NR-V2X PRR: 84% (VaN3Twin), 93–97% (legacy, neglecting realistic interference).
- (Pegurri et al., 20 May 2025)

5. Extensibility and Use Cases

VaN3Twin and related V2X DT frameworks are designed for extensibility:

System-of-systems validation: Emulate full urban deployments, geometry-specific dynamic traffic, and mixed fleets.
Spectrum sharing & interoperability: Evaluate dynamic policy-driven resource slicing, mixed DSRC/C-V2X rollouts, NR-V2X upgrades.
Hardware-in-the-loop: Support parallel digital twin predictions for live OBU deployments.
Modular physics engine: Add new propagation phenomena (RIS, mmWave, environmental effects) via standardized tracer interface.
Stack plugins: Insert novel PHY/MAC/6G V2X waveform models into ns-3 layer.

This modularity positions DT frameworks as the canonical tool for both site-specific design and scalable performance benchmarking (Pegurri et al., 20 May 2025).

6. Advanced Applications: Predictive, Proactive, and Cooperative Twin Operations

Recent work extends digital twins to proactive network management through live mobility prediction and ray-traced "what-if" scenarios:

Predictive DT operation (Pegurri et al., 23 Jan 2026): In a Tokyo campus deployment, live mobility DT with Kalman filter forecasting couples to VaN3Twin for sub-250 ms forecasting of RSSI and LoS transitions on 60 GHz links. By tightly co-designing trajectory horizons and ray-tracing fidelity, proactive resource allocation (beam steering, handover, adaptive rate/power) can be performed before environmental changes occur.
Reliability-centric multi-hop routing (Roongpraiwan et al., 2024, Roongpraiwan et al., 5 Mar 2025): Mobility DTs continuously update connectivity graphs; LSTM-driven trajectory prediction enables preemptive reconfiguration of multi-hop mmWave paths. Quantitative reliability gains reach ∼99.9% even in mixed connectivity scenarios when DT-prediction is employed.
Cybersecurity and anomaly detection (Yigit et al., 2024): Cyber-Twin architectures integrate AI-driven attack classification and resource management into the DT, achieving sub-50 ms reaction times and >98% detection rates for jamming/DDoS scenarios.

These functionalities highlight the critical role of digital twins in closed-loop, real-time net-centric V2X control and safety-critical automation.

7. Research Trends, Scalability, and Open Challenges

The progression toward city-scale V2X DTs entails the integration of edge/cloud computation, standard data modeling (Eclipse Ditto, YANG, ROS-2), and hardware-in-the-loop interfaces. Key research trajectories include:

Scalable synchronization: Mitigating data and computational bottlenecks by optimizing sensor fusion, mobility state dissemination, and federated twin updates.
Cross-layer fidelity tuning: Balancing physical-layer realism (diffuse scatter, diffraction, multipath) with real-time computational constraints through adjustable ray-tracing detail indices.
Multi-agent co-simulation: Embedding diverse traffic actors (CVs, pedestrians, infrastructure) with external and internal state modeling for in-the-loop behavior, predictive advisory, and cooperative applications (Wang et al., 2022).
Interoperability and standardization: Building on open-source, API-driven modules (ns-3, SUMO, AWSIM, Unreal Engine) to support reproducibility, cross-Vendor validation, and heterogeneous technology integration.

V2X Digital Twin Frameworks thus underpin not only high-fidelity simulation and testbed experimentation but also near-term deployment of predictive, robust, and secure vehicular networks for next-generation intelligent transportation systems.