Machine-Learning-Driven OTA Framework for Spatial Radiation Characterization of RF Circuits

Abstract

This work addresses the demand for accurate spatial radiation characterization of radio-frequency (RF) circuits and antennas by proposing an OTA (Over-The-Air) test system enhanced with machine learning techniques. The system incorporates a deep learning approach in which a fully connected deep neural network is trained using a limited set of three-dimensional measurement samples. The trained model is then used to estimate the radiation performance of the DUT across all spatial directions.To balance the number of required sampling points and the prediction accuracy of the neural model, a dynamic accuracy-validation strategy is introduced. The system gradually increases the number of training samples until the model reaches the predefined accuracy threshold.Experimental evaluations demonstrate that, compared with conventional OTA measurement systems, the proposed deep-learning-based method can accurately reconstruct the spatial radiation pattern while using only about 60% of the original sampling points. These results confirm the effectiveness, accuracy, and cost-efficiency of the proposed approach, providing a promising solution for low-cost and high-precision spatial radiation testing in RF circuit applications.