1. [Publications](/publications) 2. Sionna Research Kit: A GPU-Accelerated Research Platform for AI-RAN # Sionna Research Kit: A GPU-Accelerated Research Platform for AI-RAN ![Publication image](/sites/default/files/styles/wide/public/default_images/default.jpeg?itok=qUFsuJCP "Publication image") We introduce the NVIDIA Sionna Research Kit, a GPU-accelerated research platform for developing and testing AI/ML algorithms in 5G NR cellular networks. Powered by the NVIDIA Jetson AGX Orin, the platform leverages accelerated computing to deliver high throughput and real-time signal processing, while offering the flexibility of a software-defined stack. Built on OpenAirInterface (OAI), it unlocks a broad range of research opportunities. These include developing 5G NR and ORAN compliant algorithms, collecting real-world data for AI/ML training, and rapidly deploying innovative solutions in a very affordable testbed. Additionally, AI/ML hardware acceleration promotes the exploration of use cases in edge computing and AI radio access networks (AI-RAN). To demonstrate the capabilities, we deploy a real-time neural receiver—trained with NVIDIA Sionna and using the NVIDIA TensorRT library for inference—in a 5G NR cellular network using commercial user equipment. The code examples will be made publicly available, enabling researchers to adopt and extend the platform for their own projects. ## Authors [Sebastian Cammerer](/person/sebastian-cammerer) [Guillermo Marcus](/person/guillermo-marcus) [Tobias Zirr](/person/tobias-zirr) [Fayçal Aït Aoudia ](/person/faycal-ait-aoudia) [Lorenzo Maggi](/person/lorenzo-maggi) [Jakob Hoydis](/person/jakob-hoydis) [Alex Keller](/person/alex-keller) ## Publication Date Monday, May 26, 2025 ## Published in [2025 IEEE International Conference on Machine Learning for Communication and Ne…](https://arxiv.org/pdf/2505.15848) ## Research Area [Telecommunications](/research-area/telecommunications) ## Copyright Accepted to 2025 IEEE International Conference on Machine Learning for Communication and Networking (ICMLCN), copyright IEEE/ACM,