Sionna is a GPU-accelerated open-source library for link-level simulations based on TensorFlow. It enables the rapid prototyping of complex communication system architectures and provides native support for the integration of neural networks. Sionna implements a wide breadth of carefully tested state-of-the-art algorithms that can be used for benchmarking and end-to-end performance evaluation. This allows researchers to focus on their research, making it more impactful and reproducible, while saving time implementing components outside their area of expertise. This white paper provides a brief introduction to Sionna, explains its design principles and features, as well as future extensions, such as integrated ray tracing and custom CUDA kernels. We believe that Sionna is a valuable tool for research on next-generation communication systems, such as 6G, and we welcome contributions from our community.

翻译：Sionna是一个基于TensorFlow的GPU加速开源库，专为链路级仿真设计。它支持快速原型设计复杂通信系统架构，并提供对神经网络集成的原生支持。Sionna实现了经过严格测试的广泛尖端算法，可用于基准测试和端到端性能评估。这使得研究人员能够专注于自身研究，提升研究的影响力和可复现性，同时节省了在其专业领域之外实现组件的时间。本文简要介绍Sionna，阐述其设计原则与特性，以及未来扩展方向（如集成射线追踪和自定义CUDA内核）。我们相信Sionna是下一代通信系统（如6G）研究的宝贵工具，并欢迎社区贡献。