8 Результаты
Испытания на основе сервера R&S®SBT и быстрые ВЧ-приборы ускоряют процедуры определения характеристик и производственные испытания без снижения качества.
июн. 08, 2020
С помощью серверных испытаний (SBT) от Rohde & Schwarz клиенты могут ускорить решение измерительных задач в автоматизированных средах производства и определения характеристик компонентов. С помощью серверных испытаний (SBT) от Rohde & Schwarz клиенты могут ускорить решение измерительных задач в автоматизированных средах производства и определения характеристик компонентов.
Server-Based Testing, 5G, Speed, SBT R&S Server-Based Testing helps to reduce test times for workloads that can be parallelized.
Oct 30, 2023
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 NVIDIARohde & Schwarz and NVIDIA showcase AI/ML-based neural receiver with custom modulation at Brooklyn 6G Summit
Our server-based testing (SBT) framework, including vector signal explorer (VSE) microservices is used to pre-process the data. The generated post-FFT data is input to Nvidia's SIONNA software framework, an open-source library for 6G physical layer research.
R&S®Server-Based Testing помогает сократить длительность испытаний сигналов с несколькими несущими в 5G New Radio, поскольку каждую составляющую несущую можно анализировать независимо и параллельно.
сент. 13, 2021
Feb 21, 2023
Towards 6G: Rohde & Schwarz showcases AI/ML-based neural receiver with NVIDIA at MWC BarcelonaWith 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.
T&M solution guide for network infrastructure equipment providers