8 Risultati
I test basati su server R&S®SBT e gli strumenti RF veloci consentono di ridurre i tempi di caratterizzazione e di velocizzare i test di produzione, mantenendo la qualità.
giu 08, 2020
La soluzione Server-Based Testing (SBT) di Rohde & Schwarz aiuta i clienti ad accelerare le attività di misura negli ambienti di produzione automatizzata e per la caratterizzazione dei componenti. La soluzione Server-Based Testing (SBT) di Rohde & Schwarz aiuta i clienti ad accelerare le attività di misura negli ambienti di produzione automatizzata e per la caratterizzazione dei componenti.
Server-Based Testing, 5G, Speed, SBT R&S Server-Based Testing helps to reduce test times for workloads that can be parallelized.
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.
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
R&S®Server-Based Testing aiuta a ridurre i tempi di test dei segnali multiportante 5G New Radio: ogni portante componente può essere analizzata in modo indipendente e in parallelo.
set 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