NVIDIA Sionna

Installation Steps

Sionna requires Python and Tensorflow.

In order to run the tutorial notebooks on your machine, you also need JupyterLab.
You can alternatively test them on Google Colab.

Although not necessary, we recommend running Sionna in a Docker container.

Sionna requires TensorFlow 2.10-2.15 and Python 3.8-3.11. We recommend Ubuntu 22.04. Earlier versions of TensorFlow may still work but are not recommended because of known, unpatched CVEs.

To run the ray tracer on CPU, LLVM is required by DrJit. Please check the installation instructions for the LLVM backend.

We refer to the TensorFlow GPU support tutorial for GPU support and the required driver setup.

NVIDIA Container Toolkit

The NVIDIA Container Toolkit enables users to build and run GPU-accelerated containers. The toolkit includes a container runtime library and utilities to automatically configure containers to leverage NVIDIA GPUs.

1. Configure the production repository:

   ​​​​curl -fsSL https://nvidia.github.io/libnvidia-container/gpgkey | sudo gpg --dearmor -o /usr/share/keyrings/nvidia-container-toolkit-keyring.gpg \
   ​​​​  && curl -s -L https://nvidia.github.io/libnvidia-container/stable/deb/nvidia-container-toolkit.list | \
   ​​​​    sed 's#deb https://#deb [signed-by=/usr/share/keyrings/nvidia-container-toolkit-keyring.gpg] https://#g' | \
   ​​​​    sudo tee /etc/apt/sources.list.d/nvidia-container-toolkit.list
   

   Optionally, configure the repository to use experimental packages:

   ​​​​sed -i -e '/experimental/ s/^#//g' /etc/apt/sources.list.d/nvidia-container-toolkit.list
   

2. Update the packages list from the repository:

   ​​​​sudo apt-get update
   

3. Install the NVIDIA Container Toolkit packages:

   ​​​​sudo apt-get install -y nvidia-container-toolkit
   

Docker-based installation

1. Install Docker Engine

• Set up Docker's apt repository
  ​​​​# Add Docker's official GPG key:
  ​​​​sudo apt-get update
  ​​​​sudo apt-get install ca-certificates curl
  ​​​​sudo install -m 0755 -d /etc/apt/keyrings
  ​​​​sudo curl -fsSL https://download.docker.com/linux/ubuntu/gpg -o /etc/apt/keyrings/docker.asc
  ​​​​sudo chmod a+r /etc/apt/keyrings/docker.asc
  
  ​​​​# Add the repository to Apt sources:
  ​​​​echo \
  ​​​​  "deb [arch=$(dpkg --print-architecture) signed-by=/etc/apt/keyrings/docker.asc] https://download.docker.com/linux/ubuntu \
  ​​​​  $(. /etc/os-release && echo "$VERSION_CODENAME") stable" | \
  ​​​​  sudo tee /etc/apt/sources.list.d/docker.list > /dev/null
  ​​​​sudo apt-get update
  

• Install the Docker packages
  ​​​​sudo apt-get install docker-ce docker-ce-cli containerd.io docker-buildx-plugin docker-compose-plugin
  

• Post install to run without sudo
  ​​​​sudo groupadd docker
  

  ​​​​sudo usermod -aG docker $USER
  

  ​​​​newgrp docker
  

  ​​​​docker run hello-world
  

2. Configure the container runtime by using the nvidia-ctk command:

   ​​​​sudo nvidia-ctk runtime configure --runtime=docker
   

   The nvidia-ctk command modifies the /etc/docker/daemon.json file on the host. The file is updated so that Docker can use the NVIDIA Container Runtime.

3. Restart the Docker daemon:

   ​​​​sudo systemctl restart docker
   

4. Build the Sionna Docker image. From within the Sionna directory, run

   ​​​​make docker
   

5. Run the Docker image with GPU support

   ​​​​make run-docker gpus=all
   

   or without GPU:

   ​​​​make run-docker
   

   This will immediately launch a Docker image with Sionna installed, running JupyterLab on port 8888.

6. Browse through the example notebooks by connecting to http://10.0.0.162:8888/lab in your browser.

   

Diagram Constellation - Hello World

1. Import Sionna:

   ​​​​import os
   ​​​​gpu_num = 0 # Use "" to use the CPU
   ​​​​os.environ["CUDA_VISIBLE_DEVICES"] = f"{gpu_num}"
   ​​​​os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
   
   ​​​​# Import Sionna
   ​​​​try:
   ​​​​    import sionna
   ​​​​except ImportError as e:
   ​​​​    # Install Sionna if package is not already installed
   ​​​​    import os
   ​​​​    os.system("pip install sionna")
   ​​​​    import sionna
   
   ​​​​# IPython "magic function" for inline plots
   ​​​​%matplotlib inline
   ​​​​import matplotlib.pyplot as plt
   

2. Let us first create a BinarySource to generate a random batch of bit vectors that we can map to constellation symbols:

   ​​​​batch_size = 1000 # Number of symbols we want to generate
   ​​​​num_bits_per_symbol = 4 # 16-QAM has four bits per symbol
   ​​​​binary_source = sionna.utils.BinarySource()
   ​​​​b = binary_source([batch_size, num_bits_per_symbol])
   ​​​​b
   

3. Next, let us create a Constellation and visualize it:

   

   ​​​​constellation = sionna.mapping.Constellation("qam", num_bits_per_symbol)
   ​​​​constellation.show();
   

4. We now need a Mapper that maps each row of b to the constellation symbols according to the bit labeling shown above.

   ​​​​mapper = sionna.mapping.Mapper(constellation=constellation)
   ​​​​x = mapper(b)
   ​​​​x[:10]
   

5. Let us now make things a bit more interesting a send our symbols over and AWGN channel:

   

   ​​​​awgn = sionna.channel.AWGN()
   ​​​​ebno_db = 15 # Desired Eb/No in dB
   ​​​​no = sionna.utils.ebnodb2no(ebno_db, num_bits_per_symbol, coderate=1)
   ​​​​y = awgn([x, no])
   
   ​​​​# Visualize the received signal
   ​​​​import matplotlib.pyplot as plt
   ​​​​import numpy as np
   
   ​​​​fig = plt.figure(figsize=(7,7))
   ​​​​ax = fig.add_subplot(111)
   ​​​​plt.scatter(np.real(y), np.imag(y));
   ​​​​ax.set_aspect("equal", adjustable="box")
   ​​​​plt.xlabel("Real Part")
   ​​​​plt.ylabel("Imaginary Part")
   ​​​​plt.grid(True, which="both", axis="both")
   ​​​​plt.title("Received Symbols");
   

Sionna - Basic Operation

Getting Started with Sionna

Differentiable Communication Systems

Advanced Link-level Simulations

Toward Learned Receivers

Basic MIMO Simulations

Pulse-shaping Basics

Optical Channel with Lumped Amplification

Ray Tracing

Introduction to Sionna RT

Tutorial on Diffraction

Tutorial on Scattering