Deep Wavefront Shaping: Intelligent Control of Complex Scattering Responses with a Programmable Metasurface

Abstract

Electromagnetic environments are becoming increasingly complex and congested, creating a growing challenge for systems that rely on electromagnetic waves for communication, sensing, or imaging. The use of intelligent, reconfigurable metasurfaces provides a potential means for achieving a radio environment that is capable of directing propagating waves to optimize wireless channels on-demand, ensuring reliable operation and protecting sensitive electronic components. Here we introduce ``deep wavefront shaping'', a technique that combines a deep learning network with a binary programmable metasurface to shape waves in complex electromagnetic environments and to drive the system towards a desired scattering response. We applied this technique for wavefront reconstruction, and accurately determined metasurface configurations based on measured system scattering responses in a chaotic microwave cavity. The state of the metasurface that realizes desired electromagnetic wave field distribution properties was successfully determined even in cases previously unseen by the deep learning algorithm. Our work represents an important step towards realizing intelligent reconfigurable metasurfaces for smart radio environments that can ensure both the integrity of electronic systems and optimum performance of wireless networks.