Beam Steering for Array Antenna Based on Deep Learning

In this paper, we propose an algorithm based on deep learning for beam steering with an array antenna. Array factors are often used for beam steering problems, but because array factors cannot handle coupling influence between array antenna elements and edge effect, A deep neural network is designed to learn the physics of beam steering from data computed by array factor (AF) theory and the active element pattern (AEP). Putting the phases predicted by neural network and phases predicted by AF into simulated software to compare the main lobe direction. The results show that the trained deep neural network's output phases fulfilling the input main lobe direction requirement and it takes the coupling influence between array antenna elements into account.