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

Abstract

We propose a metasurface antenna capable of real-time holographic beam steering. An array of reconfigurable dipoles 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 iteration, 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 determine 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 200 μs to determine the meta-atomic states, thus enabling the real-time operation of a holographic antenna.

Keywords

• autoencoder
• deep learning
• green’s function
• reconfigurable metasurface antenna
• recursive born approximation