Rohde & Schwarz and NVIDIA demonstrate the importance of AI in the evolution to 6G at MWC2023

In telecommunications, a neural receiver is the concept of replacing signal processing blocks for the physical layer of a wireless communications system with trained machine learning models. With this in mind, the industry suggests that 6G standard will use AI/ML for signal processing tasks. Consequently, a neural receiver will increase link-quality and will impact throughput compared to the current high-performance deterministic software algorithms used in 5G NR.

At this year’s Mobile World Congress, Rohde & Schwarz and NVIDIA are demonstrating precisely this: how a neural receiver approach performs in a 5G NR uplink multi-user multiple input multiple output (MU-MIMO) scenario.

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 2×2 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.

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.”

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