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Based on a comparative analysis of the Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) networks, we optimize the structure of the GRU network and propose a new modulation recognition method based on feature extraction and a deep learning algorithm. High-order cumulant, Signal-to-Noise Ratio (SNR), instantaneous feature, and the cyclic spectrum of signals are extracted firstly, and then input into the Convolutional Neural Network (CNN) and the parallel network of GRU for recognition. Eight modulation modes of communication signals are recognized automatically. Simulation results show that the proposed method can achieve high recognition rate at low SNR.


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Automatic Modulation Recognition Based on CNN and GRU

Show Author's information Fugang LiuZiwei ZhangRuolin Zhou( )
Department of Electronics and Information Engineering, Heilongjiang University of Science and Technology, Harbin 150022, China
Department of Electrical and Computer Engineering, University of Massachusetts, Dartmouth, MA 02747, USA

Abstract

Based on a comparative analysis of the Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) networks, we optimize the structure of the GRU network and propose a new modulation recognition method based on feature extraction and a deep learning algorithm. High-order cumulant, Signal-to-Noise Ratio (SNR), instantaneous feature, and the cyclic spectrum of signals are extracted firstly, and then input into the Convolutional Neural Network (CNN) and the parallel network of GRU for recognition. Eight modulation modes of communication signals are recognized automatically. Simulation results show that the proposed method can achieve high recognition rate at low SNR.

Keywords:

modulation recognition, deep learning, Gated Recurrent Unit (GRU), Convolutional Neural Network (CNN)
Received: 29 October 2020 Accepted: 23 November 2020 Published: 29 September 2021 Issue date: April 2022
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Publication history

Received: 29 October 2020
Accepted: 23 November 2020
Published: 29 September 2021
Issue date: April 2022

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© The author(s) 2022

Acknowledgements

This work was partially supported by Major Scientific and Technological Achievements Transformation Project of Heilongjiang Province in 2019 (No. CG20A007).

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The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).

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