Physics-driven unsupervised deep learning network for programmable metasurface-based beamforming
Jianghan Bao,
Weihan Li,
Siqi Huang,
Wen Ming Yu,
Che Liu,
Tie Jun Cui
Affiliations
Jianghan Bao
The State Key Laboratory of Millimeter Waves, Southeast University, Nanjing 210096, China; Institute of Electromagnetic Space, Southeast University, Nanjing 210096, China
Weihan Li
The State Key Laboratory of Millimeter Waves, Southeast University, Nanjing 210096, China; Institute of Electromagnetic Space, Southeast University, Nanjing 210096, China
Siqi Huang
The State Key Laboratory of Millimeter Waves, Southeast University, Nanjing 210096, China; Institute of Electromagnetic Space, Southeast University, Nanjing 210096, China
Wen Ming Yu
The State Key Laboratory of Millimeter Waves, Southeast University, Nanjing 210096, China; Institute of Electromagnetic Space, Southeast University, Nanjing 210096, China
Che Liu
The State Key Laboratory of Millimeter Waves, Southeast University, Nanjing 210096, China; Institute of Electromagnetic Space, Southeast University, Nanjing 210096, China; Corresponding author
Tie Jun Cui
The State Key Laboratory of Millimeter Waves, Southeast University, Nanjing 210096, China; Institute of Electromagnetic Space, Southeast University, Nanjing 210096, China; Corresponding author
Summary: Programmable metasurfaces have garnered significant attention for their capacity to dynamically manipulate electromagnetic (EM) waves. In particular, the programmable metasurfaces offer to generate a wide range of EM beams when the appropriate digital coding patterns are designed. Traditionally, optimizing the coding patterns involves time-consuming nonlinear optimization algorithms due to the high computational complexity. In this study, we propose a physics-assisted deep learning (DL) model that can calculate the coding pattern in milliseconds, requiring only a simple depiction of the desired beam. An extended version of the macroscopic model for digital coding metasurface is introduced as the physics-driven component, which can compute the radiation pattern rapidly based on the provided coding pattern. The integration of the macroscopic model ensures to generate the physics-compliant coding designs. We validate the proposed method experimentally by measuring several coding patterns for both single-beam and dual-beam scenarios, which demonstrate good performance of beamforming.
