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Single-shot sub-Nyquist RF signal reconstruction based on deep learning network

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conference contribution
posted on 11.06.2019, 11:02 by S Liu, CK Mididoddi, H Zhou, B Li, W Xu, C Wang
Real-time detection of high-frequency RF signals requires sophisticated hardware with large bandwidth and high sampling rates. Existing microwave photonic methods have enabled sub-Nyquist sampling for bandwidth-efficient RF signal detection but fall short in single-shot reconstruction. Here we report a novel single-shot sub-Nyquist RF signal detection method based on a trained deep neural network. In a proof-of-concept demonstration, our system successfully reconstructs high frequency multi-toned RF signals from 5x down-sampled singleshot measurements by utilizing a deep convolutional neural network. The presented approach is a powerful digital accelerator to existing hardware detectors to significantly enhance the detection capability.

Funding

This work was supported in part by the EU FP7 Marie-Curie Career Integration Grant (631883), in part by the Royal Society (IE170007), in part by National Natural Science Foundation of China (Projects 61771148, 61571211 and U1501251), and in part by Guangzhou Science and Technology Plan (Project 201607010290)

History

Citation

2018 International Topical Meeting on Microwave Photonics (MWP), 2018

Author affiliation

/Organisation/COLLEGE OF SCIENCE AND ENGINEERING/Department of Informatics

Source

2018 International Topical Meeting on Microwave Photonics (MWP), Toulouse, France

Version

AM (Accepted Manuscript)

Published in

2018 International Topical Meeting on Microwave Photonics (MWP)

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

isbn

978-1-5386-5226-8

Acceptance date

01/10/2018

Copyright date

2018

Available date

11/06/2019

Publisher version

https://ieeexplore.ieee.org/abstract/document/8552894

Temporal coverage: start date

22/10/2018

Temporal coverage: end date

25/10/2018

Language

en