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.
At this year's Mobile World Congress, visitors can experience the first demonstration of how a neural receiver approach performs in a 5G NR uplink multi-user multiple input multiple output (MU-MIMO) scenario - a blueprint for a possible 6G physical layer. The setup combines high-end test solutions for signal generation and analysis from Rohde & Schwarz and the NVIDIA Sionna GPU-accelerated open-source library for link-level simulations.
A neural receiver constitutes the concept of replacing signal processing blocks for the physical layer of a wireless communications system with trained machine learning models. Academia, leading research institutes and industry experts across the globe anticipate that a future 6G standard will use AI/ML for signal processing tasks, such as channel estimation, channel equalization, and demapping. Today's simulations suggest that a neural receiver will increase link-quality and will impact throughput compared to the current high-performance deterministic software algorithms used in 5G NR.
To train machine learning models, data sets are an absolute prerequisite. Often, the required access to data sets is limited or simply not available. In the current state of early 6G research, test and measurement equipment provides a viable alternative when generating various data sets with different signal configurations to train machine learning models for signal processing tasks.
In the showcased AI/ML-based neural receiver setup at the Rohde & Schwarz booth, the R&S SMW200A vector signal generator emulates two individual users transmitting an 80 MHz wide signal in the uplink direction with a MIMO 2x2 signal configuration. Each user is independently faded, and noise is applied to simulate realistic radio channel conditions. The R&S MSR4 multi-purpose satellite receiver acts as the receiver, capturing the signal transmitted at a carrier frequency of 3 GHz by using its four phase-coherent receive channels. The data is then provided via the real-time streaming interface to a server. There, the signal is pre-processed using the R&S Server-Based Testing (SBT) framework including R&S VSE vector signal explorer (VSE) micro-services. The VSE signal analysis software synchronizes the signal and performs fast Fourier transforms (FFT). This post-FFT data set serves as input for a neural receiver implemented using NVIDIA Sionna.
NVIDIA Sionna is a GPU-accelerated open-source library for link-level simulation. It enables rapid prototyping of complex communications system architectures and provides native support to the integration of machine learning in 6G signal processing.
As part of the demonstration, the trained neural receiver is compared to the classical concept of a linear minimum mean squared error (LMMSE) receiver architecture, which applies traditional signal processing techniques based on deterministically developed software algorithms. These already high-performance algorithms are widely adopted in current 4G and 5G cellular networks.
Andreas Pauly, Executive Vice President of Rohde & Schwarz Test & Measurement Division, said: Signal processing in wireless communications using machine learning algorithms is a very hot topic in the industry right now, often controversially discussed among industry peers. We are delighted to work with a partner like NVIDIA on this test bed. It will enable researchers and industry experts to validate their models based on a data-driven approach and put them to the test in a hardware-in-the-loop experiment, using our leading test solutions for signal generation and analysis.
Ronnie Vasishta, Senior Vice President of Telecommunications at NVIDIA, said, Trained ML models open up considerable potential for increasing performance compared to conventional signal processing. This hardware-in-the-loop demonstration of a neural receiver from Rohde & Schwarz and NVIDIA marks a milestone for the industry in demonstrating the utility of AI and machine learning in 6G technology.
Rohde & Schwarz actively supports 6G research activities across Europe, Asia, and the US, while contributing to research projects, the work of industry alliances, and collaborating with leading research institutes and universities. The company's test and measurement expertise and solutions help pave the way for the next generation of wireless communications, which is expected to be commercially deployed around 2030.
Rohde & Schwarz will present the AI/ML based trained neural receiver demonstration at the Mobile World Congress 2023 at the Fira Gran Via in Barcelona in hall 5, booth 5A80. Visitors are welcome to witness the performance gains of the neural receiver and discuss the details and general concept with experts from Rohde & Schwarz and NVIDIA.
For further information on Rohde & Schwarz test solutions on the verge of 6G, visit:
https://www.rohde-schwarz.com/6G
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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. 
