Combining Synchrosqueezing Transform and Encoder-Decoder Classification Network for Modulation Recognition of Underwater Acoustic Communication Signals

Zixin Wang; Lingji Xu; Lixing Chen; Weihua Jiang; Fanchang Zeng; Xinzhe Chen

doi:10.1109/icspcc59353.2023.10400223

Combining Synchrosqueezing Transform and Encoder-Decoder Classification Network for Modulation Recognition of Underwater Acoustic Communication Signals

Zixin Wang, Lingji Xu, Lixing Chen, Weihua Jiang, Fanchang Zeng, Xinzhe Chen

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

In order to improve the accuracy, robustness, and practicability of modulation recognition of underwater acoustic (UWA) communication signals, a method based on the time-frequency analysis (TFA) and deep-learning framework is proposed in this paper. After simulating the modulation signals through the UWA channel, the time domain signals are pre-processed by the linear TFA method: synchrosqueezing transform (SST) that are converted into the two-dimensional TFA input data sets. The encoder-decoder classification (EDC) network is designed to extract both global and detailed features and recognize the modulation types of UWA communication signals. The simulation results illustrate that the proposed method has a good performance for classifying binary phase shift keying (BPSK), quadrature phase shift keying (QPSK), binary frequency shift keying (2FSK), quadrature frequency shift keying (4FSK), and linear frequency modulation (LFM) signals.

Original language	English
Title of host publication	Proceedings of 2023 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2023
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9798350316728
DOIs	https://doi.org/10.1109/icspcc59353.2023.10400223
State	Published - 2023
Externally published	Yes
Event	2023 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2023 - Zhengzhou, Henan, China Duration: 14 Nov 2023 → 17 Nov 2023

Publication series

Name	Proceedings of 2023 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2023

Conference

Conference	2023 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2023
Country/Territory	China
City	Zhengzhou, Henan
Period	14/11/23 → 17/11/23

Bibliographical note

Publisher Copyright:
© 2023 IEEE.

Keywords

encoder-decoder classification network
modulation recognition
time-frequency analysis

ASJC Scopus subject areas

Computer Vision and Pattern Recognition
Signal Processing
Artificial Intelligence
Computer Networks and Communications
Computer Science Applications

Access to Document

10.1109/icspcc59353.2023.10400223

Cite this

Wang, Z., Xu, L., Chen, L., Jiang, W., Zeng, F., & Chen, X. (2023). Combining Synchrosqueezing Transform and Encoder-Decoder Classification Network for Modulation Recognition of Underwater Acoustic Communication Signals. In Proceedings of 2023 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2023 (Proceedings of 2023 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2023). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/icspcc59353.2023.10400223

Wang, Zixin ; Xu, Lingji ; Chen, Lixing et al. / Combining Synchrosqueezing Transform and Encoder-Decoder Classification Network for Modulation Recognition of Underwater Acoustic Communication Signals. Proceedings of 2023 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2023. Institute of Electrical and Electronics Engineers Inc., 2023. (Proceedings of 2023 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2023).

@inproceedings{3bd6722c2eed4d5fb231d06f87ed4882,

title = "Combining Synchrosqueezing Transform and Encoder-Decoder Classification Network for Modulation Recognition of Underwater Acoustic Communication Signals",

abstract = "In order to improve the accuracy, robustness, and practicability of modulation recognition of underwater acoustic (UWA) communication signals, a method based on the time-frequency analysis (TFA) and deep-learning framework is proposed in this paper. After simulating the modulation signals through the UWA channel, the time domain signals are pre-processed by the linear TFA method: synchrosqueezing transform (SST) that are converted into the two-dimensional TFA input data sets. The encoder-decoder classification (EDC) network is designed to extract both global and detailed features and recognize the modulation types of UWA communication signals. The simulation results illustrate that the proposed method has a good performance for classifying binary phase shift keying (BPSK), quadrature phase shift keying (QPSK), binary frequency shift keying (2FSK), quadrature frequency shift keying (4FSK), and linear frequency modulation (LFM) signals.",

keywords = "encoder-decoder classification network, modulation recognition, time-frequency analysis",

author = "Zixin Wang and Lingji Xu and Lixing Chen and Weihua Jiang and Fanchang Zeng and Xinzhe Chen",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 2023 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2023 ; Conference date: 14-11-2023 Through 17-11-2023",

year = "2023",

doi = "10.1109/icspcc59353.2023.10400223",

language = "English",

series = "Proceedings of 2023 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2023",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

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Wang, Z, Xu, L, Chen, L, Jiang, W, Zeng, F & Chen, X 2023, Combining Synchrosqueezing Transform and Encoder-Decoder Classification Network for Modulation Recognition of Underwater Acoustic Communication Signals. in Proceedings of 2023 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2023. Proceedings of 2023 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2023, Institute of Electrical and Electronics Engineers Inc., 2023 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2023, Zhengzhou, Henan, China, 14/11/23. https://doi.org/10.1109/icspcc59353.2023.10400223

Combining Synchrosqueezing Transform and Encoder-Decoder Classification Network for Modulation Recognition of Underwater Acoustic Communication Signals. / Wang, Zixin; Xu, Lingji; Chen, Lixing et al.
Proceedings of 2023 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2023. Institute of Electrical and Electronics Engineers Inc., 2023. (Proceedings of 2023 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2023).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Combining Synchrosqueezing Transform and Encoder-Decoder Classification Network for Modulation Recognition of Underwater Acoustic Communication Signals

AU - Wang, Zixin

AU - Xu, Lingji

AU - Chen, Lixing

AU - Jiang, Weihua

AU - Zeng, Fanchang

AU - Chen, Xinzhe

PY - 2023

Y1 - 2023

N2 - In order to improve the accuracy, robustness, and practicability of modulation recognition of underwater acoustic (UWA) communication signals, a method based on the time-frequency analysis (TFA) and deep-learning framework is proposed in this paper. After simulating the modulation signals through the UWA channel, the time domain signals are pre-processed by the linear TFA method: synchrosqueezing transform (SST) that are converted into the two-dimensional TFA input data sets. The encoder-decoder classification (EDC) network is designed to extract both global and detailed features and recognize the modulation types of UWA communication signals. The simulation results illustrate that the proposed method has a good performance for classifying binary phase shift keying (BPSK), quadrature phase shift keying (QPSK), binary frequency shift keying (2FSK), quadrature frequency shift keying (4FSK), and linear frequency modulation (LFM) signals.

AB - In order to improve the accuracy, robustness, and practicability of modulation recognition of underwater acoustic (UWA) communication signals, a method based on the time-frequency analysis (TFA) and deep-learning framework is proposed in this paper. After simulating the modulation signals through the UWA channel, the time domain signals are pre-processed by the linear TFA method: synchrosqueezing transform (SST) that are converted into the two-dimensional TFA input data sets. The encoder-decoder classification (EDC) network is designed to extract both global and detailed features and recognize the modulation types of UWA communication signals. The simulation results illustrate that the proposed method has a good performance for classifying binary phase shift keying (BPSK), quadrature phase shift keying (QPSK), binary frequency shift keying (2FSK), quadrature frequency shift keying (4FSK), and linear frequency modulation (LFM) signals.

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KW - time-frequency analysis

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M3 - Conference contribution

AN - SCOPUS:85184851834

T3 - Proceedings of 2023 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2023

BT - Proceedings of 2023 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2023

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2023 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2023

Y2 - 14 November 2023 through 17 November 2023

ER -

Wang Z, Xu L, Chen L, Jiang W, Zeng F, Chen X. Combining Synchrosqueezing Transform and Encoder-Decoder Classification Network for Modulation Recognition of Underwater Acoustic Communication Signals. In Proceedings of 2023 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2023. Institute of Electrical and Electronics Engineers Inc. 2023. (Proceedings of 2023 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2023). doi: 10.1109/icspcc59353.2023.10400223

Combining Synchrosqueezing Transform and Encoder-Decoder Classification Network for Modulation Recognition of Underwater Acoustic Communication Signals

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

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Cite this