We propose and demonstrate a modified deep-learning-powered photonic analog-to-digital converter (DL-PADC) in which a neural network is used to eliminate the signal distortions of the photonic system. This work broadens the receiving capability from simple waveforms to complicated waveforms via implementing a modified deep learning algorithm. Thus, the modified DL-PADC can be applied in real scenarios with wideband complicated signals. Testing results show that the trained neural network eliminates the signal distortions with high quality, improving the spur-free dynamic range by ${\sim}{{20}}\;{\rm{dB}}$. An experiment for echo detection is conducted as an example, which shows that the neural network enhances the quality of detailed target profile detection. Furthermore, the modified DL-PADC only comprises a low-complexity photonic system, which obviates the requirement for redundant hardware setup while maintaining the processing quality. It is expected that the modified DL-PADC can perform as a promising photonic wideband signal receiver with low hardware complexity.

中文翻译：

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用于宽带复杂信号接收的改良型深度学习供电光子模数转换器

我们提出并演示了一种改进的深度学习供电光子模数转换器（DL-PADC），其中使用了神经网络来消除光子系统的信号失真。通过实施改进的深度学习算法，这项工作将接收能力从简单波形扩展到了复杂波形。因此，修改后的DL-PADC可以应用于宽带复杂信号的实际情况。测试结果表明，训练有素的神经网络可以高质量消除信号失真，将无杂散动态范围提高$ {\ sim} {{20}} \; {\ rm {dB}} $。以回波检测为例进行了实验，结果表明神经网络提高了详细目标轮廓检测的质量。此外，修改后的DL-PADC仅包含一个低复杂度的光子系统，从而消除了对冗余硬件设置的要求，同时保持了处理质量。期望修改后的DL-PADC可以作为有前途的光子宽带信号接收器而具有较低的硬件复杂度。