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.

中文翻译：

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用于实时全息波束控制的深度学习辅助可重构超表面天线

我们提出了一种能够进行实时全息波束控制的超表面天线。可重构偶极子阵列可以通过元原子状态的特定编码（即每个偶极子的配置）生成按需远场辐射模式。可以使用迭代来识别生成所需模式的合适状态，但这非常缓慢，需要为每个远场模式完成。在这里，我们提出了一种基于深度学习的方法来控制具有点偶极子元件的超表面天线，这些元件使用偶极子极化率改变其状态。我们采用深度学习算法，而不是迭代，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。神经网络训练采用Born近似的散射方程作为译码器，解析格林函数计算用于检验Born近似的有效性。我们基于学习的算法需要 200 μs 以内的计算时间来确定元原子状态，从而实现全息天线的实时操作。