Industrial Electronics

Keysight demonstrates 6G neural receiver design flow in collaboration with NVIDIA at Mobile World Congress 2024

27 February 2024

Keysight Technologies, Inc. has collaborated with NVIDIA to create a complete design flow for training and validating neural receivers that will be shown at Mobile World Congress Barcelona 2024. To be presented at Keysight's booth, Hall 5 Stand 5E12, the demonstration will feature an Open RAN testbed that has been augmented by a multi-user MIMO neural receiver.

While 5G integrates artificial intelligence (AI) to enhance specific components in wireless networks, 6G will be the first generation of wireless technology that is AI-native. A key goal is to develop site-specific neuralSource: Keysight Technologies, Inc.Source: Keysight Technologies, Inc. receivers to replace the entire human-designed receive chain of the physical layer. Yet, the data required to train these neural receivers is limited, and validating their performance in end-to-end systems presents a challenge. Before neural receivers can be deployed in commercial networks, they need to be adequately trained, demonstrated to surpass traditional receivers in performance and shown to robustly handle the channel conditions of real-world networks.

This demonstration shows how Keysight solutions enable the design and validation of a neural receiver. With the NVIDIA Sionna library used to train the neural receiver, ray-traced channels allow for site-specific training data generation and hence the creation of digital twins of real-world systems. As such, the neural receiver can be optimized for any intended environment.

When the training is complete, the neural receiver is deployed in an Open RAN testbed using Keysight's equipment connected to a FlexFi commercial radio unit from LITEON Technology. To emulate the site-specific channel, the Keysight PROPSIM channel emulator is used. It allows for the seamless import of ray-traced channel impulse responses. The trained neural receiver then demodulates the signal. Measurements of the block error rate of the end-to-end system provide insights into the neural receiver's performance.

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