Employing AI to optimize 6G neural receiver designs
Verifying the performance of a 6G neural receiver requires site-specific training data generation. Yet, limited data is available, and validating the receiver’s performance in end-to-end systems is challenging. Once the algorithm design is complete, designing a 6G neural receiver using artificial intelligence (AI) is a multiple-step process. Before deploying neural receivers in commercial networks, engineers must ensure that the receivers are well-trained, outperform traditional receivers, and handle the channel conditions of real-world networks robustly. AI integration in 6G focuses on two processes: channel estimation and channel state feedback.
If engineers do not understand channel behavior and fail to compensate for its anomalies in real time, 6G performance will fall consistently short of expectations. Design engineers need a solution to train neural receivers using software-generated labeled data. After generating the data, they need to validate the neural network’s performance when integrated into a wireless system. Then, they can emulate and integrate different channel conditions into the system. A channel emulator is necessary to import channel models from external tools or use existing model data. This approach enables engineers to create a digital twin of various channel conditions and compare the simulation results with a real-world system.
6G AI neural receiver design test solution
Verifying receiver functionality in 6G systems requires accurate channel estimation. The Keysight solution trains a neural receiver using labeled data generated by Keysight PathWave System Design. The system optimizes the training data to be site-specific, and the data updates to accommodate different scenarios. When the training is complete, the Keysight equipment generates and transmits new 5G waveforms to the neural receiver through a live Open RAN network, complete with a commercial ORAN radio unit. The receiver can then demodulate the signal using AI and machine learning algorithms from the previous training. Once the system processes the signal, test engineers can measure the bit error rate / block error rate for the end-to-end system to provide insights into the neural receiver’s performance. Finally, the Keysight PROPSIM channel emulator can import channel models from external ray-tracing tools. The data then serves as the digital twin of a channel to compare simulation results with a real-world system.
How to Verify the Performance of a 6G Neural Receiver
키사이트M9484C VXG 벡터 신호 발생기
M9484C VXG는 최대 54 GHz의 신호(채널당 2.5 GHz의 변조 대역폭)를 생성할 수 있는 업계 최초의 벡터 신호 생성기입니다. VXG 벡터 신호 생성기는 교정되고 동기화된 완전 통합형 솔루션으로 5G 및 위성 통신과 같은 무선 기술의 다음 단계를 제공하는 데 도움이 됩니다.
How to Verify the Performance of a 6G Neural Receiver
F8820A PROPSIM FS16 Channel Emulator
Keysight's F8820A PROPSIM FS16 RF Channel Emulator enables you to perform in-lab benchmarking of devices, base stations, digital radios, and sensor systems across the entire product workflow – from research and development to acceptance and field performance optimization
How to Verify the Performance of a 6G Neural Receiver
S5040A Open RAN Studio Player 및 Capture 어플라이언스
키사이트 S5040A는 계측기 등급 테스트 및 측정 어플라이언스로 키사이트의 PathWave Signal Studio 및 Open RAN Studio와 함께 사용하여 분산 장치(O-DU)를 에뮬레이션하고 O-RAN 업링크 통신을 캡처하고 O-RU의 기능 및 성능을 검증하는 데 필요한 측정을 수행할 수 있도록 설계되었습니다.
