# Toward Digital Network Twins: Integrating Sionna RT in ns-3 for 6G Multi-RAT Networks Simulations

arXiv: https://arxiv.org/abs/2501.00372 · HTML: https://mlsanity.com/paper/2501.00372/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Dec 2024.

## Built on by (most cited first, 6 of 6)

- [How to Bridge the Sim-to-Real Gap in Digital Twin-Aided Telecommunication Networks](https://mlsanity.com/paper/2507.07067/) (2025)
- [6GAgentGym: Tool Use, Data Synthesis, and Agentic Learning for Network Management](https://mlsanity.com/paper/2603.29656/) (2026)
- [Wideband RF Radiance Field Modeling Using Frequency-embedded 3D Gaussian Splatting](https://mlsanity.com/paper/2505.20714/) (2025)
- [AutoBS: Autonomous Base Station Deployment with Reinforcement Learning and Digital Network Twins](https://mlsanity.com/paper/2502.19647/) (2025)
- [LUCID: An Agentic AI Framework on Digital-Twin in the Loop for QoS-Guaranteeing Robotic Control](https://mlsanity.com/paper/2608.28437/) (2026)
- [Online Conformal Anomaly Detection with Prediction-Powered Data Acquisition](https://mlsanity.com/paper/2505.01783/) (2025)

## Abstract (arXiv)

The increasing complexity of 6G systems demands innovative tools for network management, simulation, and optimization. This work introduces the integration of ns-3 with Sionna RT, establishing the foundation for the first open source full-stack Digital Network Twin (DNT) capable of supporting multi-RAT. By incorporating a deterministic ray tracer for precise and site-specific channel modeling, this framework addresses limitations of traditional stochastic models and enables realistic, dynamic, and multilayered wireless network simulations. Tested in a challenging vehicular urban scenario, the proposed solution demonstrates significant improvements in accurately modeling wireless channels and their cascading effects on higher network layers. With up to 65% observed differences in application-layer performance compared to stochastic models, this work highlights the transformative potential of ray-traced simulations for 6G research, training, and network management.