Single-Shot Sub-Nyquist RF Signal Reconstruction Based on Deep Learning Network

Liu, Shun, Mididoddi, Chaitanya K, Zhou, Huiyu, Li, Baojun, Xu, Weichao, Wang, Chao (2018) Single-Shot Sub-Nyquist RF Signal Reconstruction Based on Deep Learning Network. In: 2018 International Topical Meeting on Microwave Photonics (MWP) Proceedings. . IEEE ISBN 978-1-5386-5226-8. (doi:10.1109/MWP.2018.8552894) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided) (KAR id:72606)

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Official URL: https://doi.org/10.1109/MWP.2018.8552894

Abstract

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.

Item Type:	Conference or workshop item (Proceeding)
DOI/Identification number:	10.1109/MWP.2018.8552894
Divisions:	Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Engineering and Digital Arts
Depositing User:	Chao Wang
Date Deposited:	18 Feb 2019 16:29 UTC
Last Modified:	16 Feb 2021 14:02 UTC
Resource URI:	https://kar.kent.ac.uk/id/eprint/72606 (The current URI for this page, for reference purposes)

University of Kent Author Information

Mididoddi, Chaitanya K.

Creator's ORCID:
CReDIT Contributor Roles:

Wang, Chao.

Creator's ORCID:	https://orcid.org/0000-0002-0454-8079
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