A High-Efficient Method for Synthesizing Multiple Antenna Array Radiation Patterns Simultaneously Based on Convolutional Neural Network

This paper proposes a high-efficient method that utilizes deep learning technology for synthesizing multiple antenna array radiation patterns simultaneously. More in details, the mathematical feasibility of using neural networks to optimize and synthesize radiation patterns of antenna arrays is demonstrated. Boundary functions are designed to reshape the important characteristics of target radiation patterns and transform them into a two-channel mask matrix, allowing for the simultaneous input of multiple target radiation patterns into the neural network without sacrificing computational efficiency. During training, the cost function is designed to represent the difference between each synthesized radiation pattern and the corresponding target radiation pattern, guiding self-learning. The main framework of the method is a convolutional neural network, where the convolutional layer is used to reduce the expansion of input parameters due to the simultaneous input of multiple mask matrices. Simulation experiments have been conducted to synthesize multiple incoherent target radiation patterns simultaneously on a patch antenna array layout, and the computation time is compared with the combined time required to compute each one individually. The results demonstrate that this method offers the advantage of computational efficiency for simultaneous synthesis of multiple incoherent radiation patterns.