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.

我们提出了一种能够实时全息波束引导的元表面天线。通过元原子状态的具体编码，可生成可重构偶极子阵列通过辐射在需求场模式。我们提出了一种基于深度学习的方法来控制具有变化状态的点偶极天线元表面天线。我们采用了一种深度学习算法，将自动编码器与电磁散射方程相结合，以实时确定目标远场模式所需的状态。散射方程从 Born 近似中用作训练神经网络的解码器，并使用解析格林函数计算来检查 Born 近似的有效性。我们的学习算法仅需计算时间不超过 200 微秒，即可确定元原子状态，从而实现全息天线的实时操作。