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

Shun Liu, Chaitanya K. Mididoddi, Huiyu Zhou, Baojun Li, Weichao Xu, Chao Wang*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationMWP 2018 - 2018 International Topical Meeting on Microwave Photonics
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781538652268
DOIs
Publication statusPublished - 03 Dec 2018
Externally publishedYes
Event2018 International Topical Meeting on Microwave Photonics, MWP 2018 - Toulouse, France
Duration: 22 Oct 201825 Oct 2018

Publication series

NameMWP 2018 - 2018 International Topical Meeting on Microwave Photonics

Conference

Conference2018 International Topical Meeting on Microwave Photonics, MWP 2018
CountryFrance
CityToulouse
Period22/10/201825/10/2018

Bibliographical note

Funding Information:
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).

Publisher Copyright:
© 2018 IEEE.

Copyright:
Copyright 2019 Elsevier B.V., All rights reserved.

Keywords

  • Convolutional neural network
  • deep learning
  • Nyquist sampling
  • single-shot
  • under sampling

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Signal Processing
  • Instrumentation
  • Atomic and Molecular Physics, and Optics

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