Deep-learning-assisted reconfigurable metasurface antenna for real-time holographic beam steering

TL;DR

This paper introduces a deep learning approach to control reconfigurable metasurface antennas for real-time holographic beam steering, significantly reducing computation time and enabling dynamic pattern generation.

Contribution

It presents a novel deep learning method combining an autoencoder with electromagnetic scattering equations to rapidly determine metasurface configurations for desired far field patterns.

Findings

Determines metasurface states within 200 microseconds.

Enables real-time holographic beam steering.

Uses Born approximation and Green's function for validation.

Abstract

We propose a metasurface antenna capable of real time holographic beam steering. An array of reconfigurable dipoeles can generate on demand far field patterns of radiation through the specific encoding of meta atomic states. i.e., the configuration of each dipole. Suitable states for the generation of the desired patterns can be identified using iteartion, but this is very slow and needs to be done for each far field pattern. Here, we present a deep learning based method for the control of a metasurface antenna with point dipole elements that vary in their state using dipole polarizability. Instead of iteration, we adopt a deep learning algorithm that combines an autoencoder with an electromagnetic scattering equation to determin the states required for a target far field pattern in real time. The scattering equation from Born approximation is used as the decoder in training the neural network, and analytic Green's function calculation is used to check the validity of Born approximation. Our learning based algorithm requires a computing time of within in 200 microseconds to determine the meta atomic states, thus enabling the real time opeartion of a holographic antenna.