Modified deep-learning-powered photonic analog-to-digital converter for wideband complicated signal receiving

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 ∼20dB. 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.