论文标题
Sionna：下一代物理层研究的开源库
Sionna: An Open-Source Library for Next-Generation Physical Layer Research
论文作者
论文摘要
Sionna是一个基于TensorFlow的链路级仿真的GPU加速开源库。它实现了复杂通信系统体系结构的快速原型制作，并为神经网络的集成提供了本地支持。 Sionna实现了广泛的经过精心测试的最新算法，可用于基准测试和端到端性能评估。这使研究人员能够专注于他们的研究，使其更具影响力和可再现，同时节省了在其专业领域以外实施组件的时间。这份白皮书简要介绍了Sionna，解释了其设计原理和功能以及未来的扩展，例如集成的射线跟踪和自定义CUDA内核。我们认为Sionna是研究下一代通信系统（例如6G）的宝贵工具，我们欢迎我们社区的贡献。
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.
