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

TL;DR

This paper presents an open-source Digital Network Twin integrating ns-3 and Sionna RT, enabling realistic multi-RAT 6G network simulations with site-specific channel modeling for improved accuracy.

Contribution

It introduces the first open-source full-stack Digital Network Twin combining deterministic ray tracing with ns-3 for multi-RAT 6G network simulation.

Findings

Significant improvements in channel modeling accuracy.

Up to 65% differences in application-layer performance.

Enhanced realism in wireless network simulations.

Abstract

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.