Robust and Real-Time Automatic Modulation Classification System Based on Deep Learning

Ruichen Yuan; Jian Xie

doi:10.1007/978-981-15-8155-7_349

Robust and Real-Time Automatic Modulation Classification System Based on Deep Learning

Ruichen Yuan, Jian Xie

School of Electronics and Information

Northwestern Polytechnical University Xian

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

1 Scopus citations

Abstract

Automatic modulation classification (AMC), whose main purpose is to recognize the received signal modulation mode under multi-signal environment and noise interference, provides the foundation for subsequent signal processing. In our paper, we propose a novel robust and real-time AMC implementation method based on deep learning (DL) and cognitive radio (CR), which can get faster recognition and higher accuracy. In this paper, it only needs 79.76us to recognize a set of data, and the accuracy is great than 90% at low signal-to-noise ratio.

Original language	English
Title of host publication	Advances in Guidance, Navigation and Control - Proceedings of 2020 International Conference on Guidance, Navigation and Control, ICGNC 2020
Editors	Liang Yan, Haibin Duan, Xiang Yu
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	4189-4199
Number of pages	11
ISBN (Print)	9789811581540
DOIs	https://doi.org/10.1007/978-981-15-8155-7_349
State	Published - 2022
Event	International Conference on Guidance, Navigation and Control, ICGNC 2020 - Tianjin, China Duration: 23 Oct 2020 → 25 Oct 2020

Publication series

Name	Lecture Notes in Electrical Engineering
Volume	644 LNEE
ISSN (Print)	1876-1100
ISSN (Electronic)	1876-1119

Conference

Conference	International Conference on Guidance, Navigation and Control, ICGNC 2020
Country/Territory	China
City	Tianjin
Period	23/10/20 → 25/10/20

Keywords

Automatic modulation classification
Deep learning
Software defined radio

Access to Document

10.1007/978-981-15-8155-7_349

Cite this

Yuan, R., & Xie, J. (2022). Robust and Real-Time Automatic Modulation Classification System Based on Deep Learning. In L. Yan, H. Duan, & X. Yu (Eds.), Advances in Guidance, Navigation and Control - Proceedings of 2020 International Conference on Guidance, Navigation and Control, ICGNC 2020 (pp. 4189-4199). (Lecture Notes in Electrical Engineering; Vol. 644 LNEE). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-981-15-8155-7_349

Yuan, Ruichen ; Xie, Jian. / Robust and Real-Time Automatic Modulation Classification System Based on Deep Learning. Advances in Guidance, Navigation and Control - Proceedings of 2020 International Conference on Guidance, Navigation and Control, ICGNC 2020. editor / Liang Yan ; Haibin Duan ; Xiang Yu. Springer Science and Business Media Deutschland GmbH, 2022. pp. 4189-4199 (Lecture Notes in Electrical Engineering).

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title = "Robust and Real-Time Automatic Modulation Classification System Based on Deep Learning",

abstract = "Automatic modulation classification (AMC), whose main purpose is to recognize the received signal modulation mode under multi-signal environment and noise interference, provides the foundation for subsequent signal processing. In our paper, we propose a novel robust and real-time AMC implementation method based on deep learning (DL) and cognitive radio (CR), which can get faster recognition and higher accuracy. In this paper, it only needs 79.76us to recognize a set of data, and the accuracy is great than 90% at low signal-to-noise ratio.",

keywords = "Automatic modulation classification, Deep learning, Software defined radio",

author = "Ruichen Yuan and Jian Xie",

note = "Publisher Copyright: {\textcopyright} 2022, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.; International Conference on Guidance, Navigation and Control, ICGNC 2020 ; Conference date: 23-10-2020 Through 25-10-2020",

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isbn = "9789811581540",

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publisher = "Springer Science and Business Media Deutschland GmbH",

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Yuan, R & Xie, J 2022, Robust and Real-Time Automatic Modulation Classification System Based on Deep Learning. in L Yan, H Duan & X Yu (eds), Advances in Guidance, Navigation and Control - Proceedings of 2020 International Conference on Guidance, Navigation and Control, ICGNC 2020. Lecture Notes in Electrical Engineering, vol. 644 LNEE, Springer Science and Business Media Deutschland GmbH, pp. 4189-4199, International Conference on Guidance, Navigation and Control, ICGNC 2020, Tianjin, China, 23/10/20. https://doi.org/10.1007/978-981-15-8155-7_349

Robust and Real-Time Automatic Modulation Classification System Based on Deep Learning. / Yuan, Ruichen; Xie, Jian.
Advances in Guidance, Navigation and Control - Proceedings of 2020 International Conference on Guidance, Navigation and Control, ICGNC 2020. ed. / Liang Yan; Haibin Duan; Xiang Yu. Springer Science and Business Media Deutschland GmbH, 2022. p. 4189-4199 (Lecture Notes in Electrical Engineering; Vol. 644 LNEE).

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

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AU - Xie, Jian

PY - 2022

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N2 - Automatic modulation classification (AMC), whose main purpose is to recognize the received signal modulation mode under multi-signal environment and noise interference, provides the foundation for subsequent signal processing. In our paper, we propose a novel robust and real-time AMC implementation method based on deep learning (DL) and cognitive radio (CR), which can get faster recognition and higher accuracy. In this paper, it only needs 79.76us to recognize a set of data, and the accuracy is great than 90% at low signal-to-noise ratio.

AB - Automatic modulation classification (AMC), whose main purpose is to recognize the received signal modulation mode under multi-signal environment and noise interference, provides the foundation for subsequent signal processing. In our paper, we propose a novel robust and real-time AMC implementation method based on deep learning (DL) and cognitive radio (CR), which can get faster recognition and higher accuracy. In this paper, it only needs 79.76us to recognize a set of data, and the accuracy is great than 90% at low signal-to-noise ratio.

KW - Automatic modulation classification

KW - Deep learning

KW - Software defined radio

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SN - 9789811581540

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BT - Advances in Guidance, Navigation and Control - Proceedings of 2020 International Conference on Guidance, Navigation and Control, ICGNC 2020

A2 - Yan, Liang

A2 - Duan, Haibin

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PB - Springer Science and Business Media Deutschland GmbH

T2 - International Conference on Guidance, Navigation and Control, ICGNC 2020

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Yuan R, Xie J. Robust and Real-Time Automatic Modulation Classification System Based on Deep Learning. In Yan L, Duan H, Yu X, editors, Advances in Guidance, Navigation and Control - Proceedings of 2020 International Conference on Guidance, Navigation and Control, ICGNC 2020. Springer Science and Business Media Deutschland GmbH. 2022. p. 4189-4199. (Lecture Notes in Electrical Engineering). doi: 10.1007/978-981-15-8155-7_349

Robust and Real-Time Automatic Modulation Classification System Based on Deep Learning

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