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 impl...

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Bibliographic Details
Published in:Optics letters 2020-10, Vol.45 (19), p.5303-5306
Main Authors: Xu, Shaofu, Wan, Jun, Wang, Rui, Zou, Weiwen
Format: Article
Language:English
Subjects:
Algorithms
Analog to digital conversion
Analog to digital converters
Broadband
Complexity
Deep learning
Hardware
Neural networks
Photonics
Receiving
Signal distortion
Signal quality
Target detection
Waveforms
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Summary: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 ∼ 20 d B . 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.
ISSN:0146-9592
1539-4794
DOI:10.1364/OL.405367