Intelligent Beamforming via Physics-Inspired Neural Networks on Programmable Metasurface

Abstract

Programmable metasurface (PM) exhibits powerful capabilities to manipulate electromagnetic (EM) waves with controlled active components loaded on the sub wavelength elements. However, for electrically large metasurface with complex structures, it is difficult to implement the fast mapping from the codes, i.e., states of the active components, to radiation patterns and vice versa. In this article, artificial neural networks are employed to realize code-to-pattern (C-P) and pattern-to-code (P-C) mapping accurately and efficiently. As for the C-P mapping, a novel physics-inspired neural network (PINN), which is primarily inspired by the discrete dipole approximation (DDA) method, is proposed. The PINN is physically interpretable and shows a strong few-shot learning ability. The average error between the patterns predicted by the PINN and measured patterns is as low as 2.31 dB. For the P-C mapping, a deep neural network (DNN) is proposed with PINN as its teacher network, i.e., PINN is used to generate more radiation patterns to train the DNN. The average accuracy of codes prediction is higher than 98.4%. Finally, an intelligent beamforming scheme is implemented by combining the PINN and DNN. For the desired patterns, the required codes could be accurately calculated by the DNN in real time. Then, the synthesized patterns of the required codes could be achieved by the PINN to compare with the desired patterns. The proposed scheme is a first step toward practical applications of PM in the fields of sensing and communication.