This is an ns-3 version implementing ray tracing for wireless channel simulation using NVIDIA Sionna RT.

The integration of Sionna RT enables highly accurate propagation modeling by leveraging GPU-accelerated ray tracing, making it ideal for simulations in complex environments at any frequency, including urban and vehicular scenarios.

For details about this integration running in a possible application example, refer to the following paper:

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

• Deterministic Wireless Channel Simulations using the GPU-accelerated RT module in Sionna. Even if Sionna has native support for GPUs via TensorFlow, it can also run on CPU only.
• Fully Customizable Scenario in Sionna RT with the possibility of chosing object meshes, materials (more details about scenes available here) and antennas.
• Seamless Mobility Syncronization between ns-3 Nodes and their correspondent mesh in Sionna RT.
• Possibility to run Sionna RT and ns-3 on two different machines for example with Sionna RT in a GPU-powered server farm to reduce computation times.

ns-3 and Sionna are two separated entities able to communicate with each other via an UDP network socket. For this reason, the installation consists in two steps:

A complete ns-3 installation is contained in this repository, as well as the sionna module for the integration with Sionna RT.

Use the following commands to download and build ns3-rt:

git clone https://github.com/robpegurri/ns3-rt.git
cd ns3-rt
./ns3 configure --disable-python --enable-examples 
./ns3 build

Sionna RT v1.0.1 and later has the same requiremets as Mitsuba 3 available at this page. Detailed instructions are available in the official Sionna RT installation guide. Ubuntu 22.04 is recommended.

If you are running Sionna RT on a CPU, install TensorFlow and LLVM (also required in this case) with:

sudo apt install llvm
python3 -m pip install tensorflow 

and verify the installation with:

python3 -c "import tensorflow as tf; print(tf.reduce_sum(tf.random.normal([1000, 1000])))"

If you are running Sionna RT on a GPU, install the required drivers (refer to your GPU vendor). After the drivers are properly setup, TensorFlow GPU can be installed with:

python3 -m pip install 'tensorflow[and-cuda]' 

and verify the installation with:

python3 -c "import tensorflow as tf; print(tf.config.list_physical_devices('GPU'))"

In case of any issues with TensorFlow or TensorFlow GPU, please refer to the official installation guide at this page.

At this point, install Sionna RT with:

pip install sionna-rt

To run the simple-sionna-example example, you first need to start Sionna RT (this example expects Sionna to run locally, see the next section to know how to run Sionna RT remotely).

Run the Python example script sionna_server_v1_script.py from the /src/sionna folder with the following command and options:

python3 'sionna_server_v1_script.py' --local-machine --frequency=2.1e9 --path-to-xml-scenario=scenarios/SionnaExampleScenario/scene.xml

This script requires at least Sionna RT v1.0.1. If for any reason you are running Sionna RT v0.19.2, run the sionna_v0_server_script.py under the same folder.

After Sionna RT has started, run the ns-3 simulation in parallel with:

./ns3 run simple-sionna-example

The user is expected to take the sionna_v1_server_script.py as a reference for the creation of its own script for simulation.

ns3-rt was created with the possibility to run Sionna RT both locally (on the same machine with ns-3) and remotely (in a server with). In your ns-3 script, you can enable this possibility with SionnaHelper this way:

#include "ns3/sionna-helper.h"
...
SionnaHelper& sionnaHelper = SionnaHelper::GetInstance();
sionnaHelper.SetLocalMachine(false);
sionnaHelper.SetServerIp("YOUR-IP-ADDRESS-HERE");

While on Sionna RT side, just remove the --local-machine flag when running the Python script. The default port used by ns3-sionna is UDP/8103.

ns3-rt links every Node to a specific object in Sionna RT (associated with a fully customizable mesh). If this mesh is not found in the scene, then Sionna would not know how to calculate any of the requested values by ns3-rt.

In the given example, upon the reception of a LOC_UPDATE message from ns3-rt, sionna_v1_server_script.py (or the equivalent version for Sionna RT v0.19.2) looks for the correspondent object mesh named car_n, where n is calculated as the ns-3 Node ID + 1. The TX and RX antennas are placed on top of the objects (cars, in this case).

To fully understand how to create a custom scene for Sionna RT, please refer to the official video tutorial by NVIDIA.

If you want to acknowledge our work, please refer to the following publication:

@INPROCEEDINGS{Pegu2505:Toward,
AUTHOR="Roberto Pegurri and Francesco Linsalata and Eugenio Moro and Jakob Hoydis
and Umberto Spagnolini",
TITLE="Toward Digital Network Twins: Integrating Sionna {RT} in ns-3 for {6G}
{Multi-RAT} Networks Simulations",
BOOKTITLE="IEEE INFOCOM WKSHPS: Digital Twins over NextG Wireless Networks (DTWIN
2025) (INFOCOM DTWIN 2025)",
ADDRESS="London, United Kingdom (Great Britain)",
PAGES="5.88",
DAYS=18,
MONTH=may,
YEAR=2025
}