Automatic Modulation Recognition Based on CNN and GRU

Fugang Liu; Ziwei Zhang; Ruolin Zhou

doi:10.26599/TST.2020.9010057

Tsinghua Science and Technology 2022, 27(2): 422-431 https://doi.org/10.26599/TST.2020.9010057

Open Access | Issue | Published: 29 September 2021

Automatic Modulation Recognition Based on CNN and GRU

Show Author's Information Hide Author's Information Fugang Liu, Ziwei Zhang, Ruolin 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

Keywords:

Convolutional Neural Network (CNN), deep learning, modulation recognition, Gated Recurrent Unit (GRU)

Cite this article:

Liu F, Zhang Z, Zhou R. Automatic Modulation Recognition Based on CNN and GRU. Tsinghua Science and Technology, 2022, 27(2): 422-431. https://doi.org/10.26599/TST.2020.9010057

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Abstract Full text About this article

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.

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Automatic Modulation Recognition Based on CNN and GRU

Show Author's information Hide Author's Information Fugang Liu, Ziwei Zhang, Ruolin 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

Keywords: Convolutional Neural Network (CNN), deep learning, modulation recognition, Gated Recurrent Unit (GRU)

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Acknowledgements

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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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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/).