Sionna

Sionna™ is a hardware-accelerated differentiable open-source library for research on communication systems. It is composed of the following modules:

  • Sionna RT: A lightning-fast stand-alone ray tracer for radio propagation modeling

  • Sionna PHY: A link-level simulator for wireless and optical communication systems

  • Sionna SYS: System-level simulation functionalities based on physical-layer abstraction

The core principles of Sionna are modularity, extensibility, and differentiability.

Every building block is an independent module that can be easily tested, understood, and modified according to your needs. The documentation is complete and includes references. Similar to constructing a deep neural network by stacking different layers, complex communication system architectures can be rapidly prototyped by connecting the desired blocks.

Sionna PHY and Sionna SYS are written in Tensorflow, while Sionna RT is built on top of Mitsuba 3 and Dr.Jit. These frameworks provide automatic differentiation and can backpropagate gradients through an entire system. This is the key enabler for gradient-based optimization and machine learning, especially the integration of neural networks.

NVIDIA GPU acceleration provides orders-of-magnitude faster simulation, enabling the interactive exploration of such systems, for example, in Jupyter notebooks that can be run on cloud services such as Google Colab. If no GPU is available, Sionna will run on the CPU.

Sionna is developed, continuously extended, and used by NVIDIA to drive 5G and 6G research.

License

Sionna is Apache-2.0 licensed, as found in the LICENSE file.