Programmable metasurfaces have recently been proposed to dynamically manipulate electromagnetic (EM) waves in both temporal and spatial dimensions. With active components integrated into unit cells of the metasurface, states of the unit cells can be adjusted by digital codes. The metasurface can then construct complex spatial and temporal electromagnetic beams. Given the main parameters of the beam, the optimal codes can be computed by nonlinear optimization algorithms, such as genetic algorithm, particle swarm optimization, etc. The high computational complexity of these algorithms makes it very challenging to compute the codes in real time. In this study, we applied deep learning techniques to compute the codes. A deep convolutional neural network is designed and trained to compute the required element codes in milliseconds, given the requirement of the waveform. The average accuracy of the prediction reaches more than 94 percent. This scheme is validated on a 1-bit programmable metasurface and both experimental and numerical results agree with each other well. This study shows that machines may “learn” the physics of modulating electromagnetic waves with the help of the good generalization ability in deep convolutional neural networks. The proposed scheme may provide us with a possible solution for real-time complex beamforming in antenna arrays, such as the programmable metasurface.

中文翻译：

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基于深度学习技术的可编程超表面编码

最近有人提出可编程超表面来动态操纵时间和空间维度的电磁 (EM) 波。通过将活性成分集成到超表面的单元中，可以通过数字代码调整单元的状态。然后，超表面可以构建复杂的空间和时间电磁波束。给定光束的主要参数，可以通过非线性优化算法计算出最优编码，如遗传算法、粒子群优化等。这些算法的高计算复杂度使得实时计算编码变得非常具有挑战性。在这项研究中，我们应用深度学习技术来计算代码。设计和训练深度卷积神经网络以在毫秒内计算所需的元素代码，给定波形的要求。预测的平均准确率达到94%以上。该方案在 1 位可编程超表面上得到验证，实验结果和数值结果相互吻合。这项研究表明，机器可以借助深度卷积神经网络良好的泛化能力“学习”调制电磁波的物理原理。所提出的方案可以为我们提供一种可能的解决方案，用于天线阵列中的实时复杂波束成形，例如可编程超表面。这项研究表明，机器可以借助深度卷积神经网络良好的泛化能力“学习”调制电磁波的物理原理。所提出的方案可以为我们提供一种可能的解决方案，用于天线阵列中的实时复杂波束成形，例如可编程超表面。这项研究表明，机器可以借助深度卷积神经网络良好的泛化能力“学习”调制电磁波的物理原理。所提出的方案可以为我们提供一种可能的解决方案，用于天线阵列中的实时复杂波束成形，例如可编程超表面。