Modulation Format Identification Based on Channel-spatial Attention Modules and Deep Learning

Qian Chen; Qi Zhang; Xiangjun Xin; Yi Cui; Fu Wang; Feng Tian; Qinghua Tian; Yongjun Wang; Leijing Yang

doi:10.1109/ICCASIT58768.2023.10351587

Modulation Format Identification Based on Channel-spatial Attention Modules and Deep Learning

Qian Chen, Qi Zhang^*, Xiangjun Xin, Yi Cui, Fu Wang, Feng Tian, Qinghua Tian, Yongjun Wang, Leijing Yang

^*Corresponding author for this work

School of Information and Electronics

Beijing University of Posts and Telecommunications

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

Abstract

In this paper, a scheme is proposed where the channel and spatial attention convolutional neural networks are applied to identify modulation formats from signal constellation diagrams. According to the simulation results, it outperforms other modulation format identification (MFI) schemes in the overall identification rate. Moreover, the scheme shows a significant advantage in signal identification given low optical signal-to-noise ratios (OSNRs).

Original language	English
Title of host publication	Proceedings of 2023 IEEE 5th International Conference on Civil Aviation Safety and Information Technology, ICCASIT 2023
Editors	Huabo Sun
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	887-891
Number of pages	5
ISBN (Electronic)	9798350310603
DOIs	https://doi.org/10.1109/ICCASIT58768.2023.10351587
Publication status	Published - 2023
Event	5th IEEE International Conference on Civil Aviation Safety and Information Technology, ICCASIT 2023 - Dali, China Duration: 11 Oct 2023 → 13 Oct 2023

Publication series

Name	Proceedings of 2023 IEEE 5th International Conference on Civil Aviation Safety and Information Technology, ICCASIT 2023

Conference

Conference	5th IEEE International Conference on Civil Aviation Safety and Information Technology, ICCASIT 2023
Country/Territory	China
City	Dali
Period	11/10/23 → 13/10/23

Keywords

convolutional neural networks
modulation format identification
signal constellation diagrams

Access to Document

10.1109/ICCASIT58768.2023.10351587

Cite this

Chen, Q., Zhang, Q., Xin, X., Cui, Y., Wang, F., Tian, F., Tian, Q., Wang, Y., & Yang, L. (2023). Modulation Format Identification Based on Channel-spatial Attention Modules and Deep Learning. In H. Sun (Ed.), Proceedings of 2023 IEEE 5th International Conference on Civil Aviation Safety and Information Technology, ICCASIT 2023 (pp. 887-891). (Proceedings of 2023 IEEE 5th International Conference on Civil Aviation Safety and Information Technology, ICCASIT 2023). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICCASIT58768.2023.10351587

Chen, Qian ; Zhang, Qi ; Xin, Xiangjun et al. / Modulation Format Identification Based on Channel-spatial Attention Modules and Deep Learning. Proceedings of 2023 IEEE 5th International Conference on Civil Aviation Safety and Information Technology, ICCASIT 2023. editor / Huabo Sun. Institute of Electrical and Electronics Engineers Inc., 2023. pp. 887-891 (Proceedings of 2023 IEEE 5th International Conference on Civil Aviation Safety and Information Technology, ICCASIT 2023).

@inproceedings{50c2345f74cd488e91a9b0e885bc0ecc,

title = "Modulation Format Identification Based on Channel-spatial Attention Modules and Deep Learning",

abstract = "In this paper, a scheme is proposed where the channel and spatial attention convolutional neural networks are applied to identify modulation formats from signal constellation diagrams. According to the simulation results, it outperforms other modulation format identification (MFI) schemes in the overall identification rate. Moreover, the scheme shows a significant advantage in signal identification given low optical signal-to-noise ratios (OSNRs).",

keywords = "convolutional neural networks, modulation format identification, signal constellation diagrams",

author = "Qian Chen and Qi Zhang and Xiangjun Xin and Yi Cui and Fu Wang and Feng Tian and Qinghua Tian and Yongjun Wang and Leijing Yang",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 5th IEEE International Conference on Civil Aviation Safety and Information Technology, ICCASIT 2023 ; Conference date: 11-10-2023 Through 13-10-2023",

year = "2023",

doi = "10.1109/ICCASIT58768.2023.10351587",

language = "English",

series = "Proceedings of 2023 IEEE 5th International Conference on Civil Aviation Safety and Information Technology, ICCASIT 2023",

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

pages = "887--891",

editor = "Huabo Sun",

booktitle = "Proceedings of 2023 IEEE 5th International Conference on Civil Aviation Safety and Information Technology, ICCASIT 2023",

address = "United States",

}

Chen, Q, Zhang, Q, Xin, X, Cui, Y, Wang, F, Tian, F, Tian, Q, Wang, Y & Yang, L 2023, Modulation Format Identification Based on Channel-spatial Attention Modules and Deep Learning. in H Sun (ed.), Proceedings of 2023 IEEE 5th International Conference on Civil Aviation Safety and Information Technology, ICCASIT 2023. Proceedings of 2023 IEEE 5th International Conference on Civil Aviation Safety and Information Technology, ICCASIT 2023, Institute of Electrical and Electronics Engineers Inc., pp. 887-891, 5th IEEE International Conference on Civil Aviation Safety and Information Technology, ICCASIT 2023, Dali, China, 11/10/23. https://doi.org/10.1109/ICCASIT58768.2023.10351587

Modulation Format Identification Based on Channel-spatial Attention Modules and Deep Learning. / Chen, Qian; Zhang, Qi; Xin, Xiangjun et al.
Proceedings of 2023 IEEE 5th International Conference on Civil Aviation Safety and Information Technology, ICCASIT 2023. ed. / Huabo Sun. Institute of Electrical and Electronics Engineers Inc., 2023. p. 887-891 (Proceedings of 2023 IEEE 5th International Conference on Civil Aviation Safety and Information Technology, ICCASIT 2023).

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

TY - GEN

T1 - Modulation Format Identification Based on Channel-spatial Attention Modules and Deep Learning

AU - Chen, Qian

AU - Zhang, Qi

AU - Xin, Xiangjun

AU - Cui, Yi

AU - Wang, Fu

AU - Tian, Feng

AU - Tian, Qinghua

AU - Wang, Yongjun

AU - Yang, Leijing

PY - 2023

Y1 - 2023

N2 - In this paper, a scheme is proposed where the channel and spatial attention convolutional neural networks are applied to identify modulation formats from signal constellation diagrams. According to the simulation results, it outperforms other modulation format identification (MFI) schemes in the overall identification rate. Moreover, the scheme shows a significant advantage in signal identification given low optical signal-to-noise ratios (OSNRs).

AB - In this paper, a scheme is proposed where the channel and spatial attention convolutional neural networks are applied to identify modulation formats from signal constellation diagrams. According to the simulation results, it outperforms other modulation format identification (MFI) schemes in the overall identification rate. Moreover, the scheme shows a significant advantage in signal identification given low optical signal-to-noise ratios (OSNRs).

KW - convolutional neural networks

KW - modulation format identification

KW - signal constellation diagrams

UR - http://www.scopus.com/inward/record.url?scp=85182257486&partnerID=8YFLogxK

U2 - 10.1109/ICCASIT58768.2023.10351587

DO - 10.1109/ICCASIT58768.2023.10351587

M3 - Conference contribution

AN - SCOPUS:85182257486

T3 - Proceedings of 2023 IEEE 5th International Conference on Civil Aviation Safety and Information Technology, ICCASIT 2023

SP - 887

EP - 891

BT - Proceedings of 2023 IEEE 5th International Conference on Civil Aviation Safety and Information Technology, ICCASIT 2023

A2 - Sun, Huabo

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 5th IEEE International Conference on Civil Aviation Safety and Information Technology, ICCASIT 2023

Y2 - 11 October 2023 through 13 October 2023

ER -

Chen Q, Zhang Q, Xin X, Cui Y, Wang F, Tian F et al. Modulation Format Identification Based on Channel-spatial Attention Modules and Deep Learning. In Sun H, editor, Proceedings of 2023 IEEE 5th International Conference on Civil Aviation Safety and Information Technology, ICCASIT 2023. Institute of Electrical and Electronics Engineers Inc. 2023. p. 887-891. (Proceedings of 2023 IEEE 5th International Conference on Civil Aviation Safety and Information Technology, ICCASIT 2023). doi: 10.1109/ICCASIT58768.2023.10351587

Modulation Format Identification Based on Channel-spatial Attention Modules and Deep Learning

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