Efficient Radiation Pattern Prediction of Array Antennas Based on Complex-Valued Graph Neural Networks
Jie Jin, Qian Su, Yan Xu, Zhengrui He, Yu Lu
Abstract
Antenna design with the traditional numerical methods is a highly computationally expensive task. In this letter, a complex-valued graph neural network (GNN) with residual connections is proposed to efficiently predict radiation pattern for antenna arrays with different geometric structures. The model uses GNN as a backbone, and the topological structure and excitation of the antenna array are encoded into a graph representation. Complex weights and residual connections are designed to improve the accuracy of the model. Experiments are conducted on five kinds of arrays under amplitude–phase control. The experimental results show that the average mean absolute percentage error and the average root-mean-square error of the model on five types of the antenna array are 1.86% and 0.032, respectively, while the time efficiency of the model is six orders of magnitude faster than that of the traditional full-wave simulation.