Oct 30, 2023 | Comunicato stampa | Wireless communications

Enabling an AI-native air interface for 6G: Rohde & Schwarz showcases AI/ML-based neural receiver with optimized modulation at Brooklyn 6G Summit, in collaboration with NVIDIA

With research on the technological components of the future 6G wireless communication standard in full swing, the possibilities of an AI-native air interface for 6G are also being explored. In collaboration with NVIDIA, Rohde & Schwarz takes another step forward and presents an enhancement to its recent hardware-in-the-loop demonstration of a neural receiver, showing the achievable performance gains when using trained ML models compared to traditional signal processing — while for the first time also optimizing the transmitter side.

May 22, 2025 | Comunicato stampa | Aerospace

Rohde & Schwarz Satellite Industry Day 2025: Connecting the world with New Space and 5G NTN technologies

After four successful online events with over 1000 participants, Rohde & Schwarz is hosting its fifth Satellite Industry Day on June 3, 2025, on-site at its Munich campus. Rohde & Schwarz test and measurement experts and partners from the industry will present topics from 5G Non-Terresterial Network (NTN) and satellite testing to monitoring and regulatory issues. During breaks participants can experience cutting-edge test and measurement solutions.

Feb 21, 2023 | Comunicato stampa | Test & measurement

Towards 6G: Rohde & Schwarz showcases AI/ML-based neural receiver with NVIDIA at MWC Barcelona

With research on the technology components for the future 6G wireless communication standard in full swing, the possibilities of an AI-native air interface for 6G also are being explored. Rohde & Schwarz, working with NVIDIA, is taking a step forward from simulations to implementing artificial intelligence and machine learning (AI/ML) in future 6G technology. At MWC Barcelona, the companies will present the industry’s first hardware-in-the-loop demonstration of a neural receiver, showing the achievable performance gains when using trained ML models compared to traditional signal processing.