基于深度学习的射频电路空间辐射测试系统

Deep learning-based over-the-air measurement system for RF circuitries

针对射频电路及天线的辐射性能测试需求,提出一种基于机器学习的射频电路空间辐射OTA测试系统。系统引入深度学习方法,利用三维空间有限测量数据训练一个全连接深度神经网络(FCDNN)模型,从而估计被测射频电路系统在三维空间各个方向上的辐射性能。为了权衡训练FCDNN模型所需的测试点数量与模型预测结果的准确度,进一步提出动态检验模型准确度,逐步提升训练测试点数量,直到模型精度达到预设要求的解决办法。实验结果表明,相比于现有OTA测试系统,所提出的基于深度学习的测试系统只需约60%的测试点,就能精准重构被测射频电路的空间辐射性能,验证了该方案的准确性与高效性,为行业提供了一种精确却低成本的空间辐射测试技术解决方案。

To effectively evaluate the radiation performance of RF front-end circuitries as demanded by the wireless industry,this article proposes a deep learning-based over-the-air(OTA)measurement system.By training a fully-connected deep neural network(FCDNN)with radiation measurements in some test points,we are able to accurately estimate the radiation performance of a RF circuitry in all 3D directions.To balance between the number of radiation measurements for FCDNN training and the estimation accuracy,we further propose to dynamically evaluate the accuracy of the trained model and increase the number of training radiation measurements,until the trained mode can satisfy a predefined accuracy.Experimental results show that the proposed OTA measurement system can accurately reconstruct the radiation performance of a RF circuitry with approximately 60%test points as compared to traditional methods.The proposed OTA measurement system can provide an accurate but cost-effective radiation measurement solution for the wireless industry.