We show an adaptive deep-learning algorithm that recovers the distorted broadband signals of defective microwave photonic (MWP) receiving systems. With data-driven supervised training, the adopted neural network automatically learns the end-to-end distortion effects of the photonic analog links and recovers the received signals in the digital domain. Through changing the training data sets and retraining the same neural network, this algorithm can be applied in various MWP receiving systems. Two MWP receiving systems are set up for experimentally demonstrating the capability of broadband signal recovery. Results show that the neural network can reduce the signal distortion (measured with mean square error) by ${\sim}{18}\;{\rm dB}$. Moreover, visualization analysis indicates that the proposed algorithm is potentially adaptive to more MWP receiving systems and applications. The noise robustness of this algorithm is also verified so that it is applicable in noisy situations. The proposed algorithm improves the performance of MWP receiving systems through appending a deep learning digital processor whose deployment is low cost.

中文翻译：

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基于监督训练的宽带微波光子接收系统信号恢复的自适应深度学习算法

我们展示了一种自适应的深度学习算法，该算法可恢复有缺陷的微波光子（MWP）接收系统的失真宽带信号。通过数据驱动的有监督的训练，采用的神经网络可以自动学习光子模拟链路的端到端失真效应，并在数字域中恢复接收到的信号。通过更改训练数据集并重新训练同一神经网络，该算法可以应用于各种MWP接收系统。建立了两个MWP接收系统，以通过实验证明宽带信号恢复的能力。结果表明，神经网络可以将信号失真（用均方误差测量）降低$ {\ sim} {18} \; {\ rm dB} $。此外，可视化分析表明，所提出的算法潜在地适用于更多的MWP接收系统和应用。还验证了该算法的噪声鲁棒性，使其适用于嘈杂的情况。所提出的算法通过添加部署成本低的深度学习数字处理器来提高MWP接收系统的性能。