Autocorrelation Convolution Networks Based on Deep Learning for Automatic Modulation Classification

Duona Zhang; Wenrui Ding; Hongyu Wang; Baochang Zhang

doi:10.1109/ICIEA48937.2020.9248386

Autocorrelation Convolution Networks Based on Deep Learning for Automatic Modulation Classification

Duona Zhang
, Wenrui Ding
, Hongyu Wang
, Baochang Zhang

Beihang University

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

4 Scopus citations

Abstract

Automatic modulation classification (AMC) is challenging but significant in the field of cognitive radio. Despite recent deep learning methods have dominated as the best performers for AMC, they are challenged by the practical problem in low signal-to-noise ratios (SNRs). In this paper, we propose novel autocorrelation convolution networks (ACNs) to capture periodic representation for communication signals. In ACNs, modulation modes are classified with the periodic local features under an autocorrelation convolution criterion. The experimental results demonstrate that ACNs achieve a great improvement that outperforms recent deep learning methods in low SNRs.

Original language	English
Title of host publication	Proceedings of the 15th IEEE Conference on Industrial Electronics and Applications, ICIEA 2020
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1561-1565
Number of pages	5
ISBN (Electronic)	9781728151694
DOIs	https://doi.org/10.1109/ICIEA48937.2020.9248386
State	Published - 9 Nov 2020
Event	15th IEEE Conference on Industrial Electronics and Applications, ICIEA 2020 - Virtual, Kristiansand, Norway Duration: 9 Nov 2020 → 13 Nov 2020

Publication series

Name	Proceedings of the 15th IEEE Conference on Industrial Electronics and Applications, ICIEA 2020

Conference

Conference	15th IEEE Conference on Industrial Electronics and Applications, ICIEA 2020
Country/Territory	Norway
City	Virtual, Kristiansand
Period	9/11/20 → 13/11/20

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Keywords

cognitive radio
Deep learning
modulation classification
wireless communication

Access to Document

10.1109/ICIEA48937.2020.9248386

Cite this

Zhang, D., Ding, W., Wang, H., & Zhang, B. (2020). Autocorrelation Convolution Networks Based on Deep Learning for Automatic Modulation Classification. In Proceedings of the 15th IEEE Conference on Industrial Electronics and Applications, ICIEA 2020 (pp. 1561-1565). Article 9248386 (Proceedings of the 15th IEEE Conference on Industrial Electronics and Applications, ICIEA 2020). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICIEA48937.2020.9248386

Zhang, Duona ; Ding, Wenrui ; Wang, Hongyu et al. / Autocorrelation Convolution Networks Based on Deep Learning for Automatic Modulation Classification. Proceedings of the 15th IEEE Conference on Industrial Electronics and Applications, ICIEA 2020. Institute of Electrical and Electronics Engineers Inc., 2020. pp. 1561-1565 (Proceedings of the 15th IEEE Conference on Industrial Electronics and Applications, ICIEA 2020).

@inproceedings{6bd8bce4b6b14e3a85eb4d74e3415616,

title = "Autocorrelation Convolution Networks Based on Deep Learning for Automatic Modulation Classification",

abstract = "Automatic modulation classification (AMC) is challenging but significant in the field of cognitive radio. Despite recent deep learning methods have dominated as the best performers for AMC, they are challenged by the practical problem in low signal-to-noise ratios (SNRs). In this paper, we propose novel autocorrelation convolution networks (ACNs) to capture periodic representation for communication signals. In ACNs, modulation modes are classified with the periodic local features under an autocorrelation convolution criterion. The experimental results demonstrate that ACNs achieve a great improvement that outperforms recent deep learning methods in low SNRs.",

keywords = "cognitive radio, Deep learning, modulation classification, wireless communication",

author = "Duona Zhang and Wenrui Ding and Hongyu Wang and Baochang Zhang",

note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 15th IEEE Conference on Industrial Electronics and Applications, ICIEA 2020 ; Conference date: 09-11-2020 Through 13-11-2020",

year = "2020",

month = nov,

day = "9",

doi = "10.1109/ICIEA48937.2020.9248386",

language = "英语",

series = "Proceedings of the 15th IEEE Conference on Industrial Electronics and Applications, ICIEA 2020",

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

pages = "1561--1565",

booktitle = "Proceedings of the 15th IEEE Conference on Industrial Electronics and Applications, ICIEA 2020",

address = "美国",

}

Zhang, D, Ding, W, Wang, H & Zhang, B 2020, Autocorrelation Convolution Networks Based on Deep Learning for Automatic Modulation Classification. in Proceedings of the 15th IEEE Conference on Industrial Electronics and Applications, ICIEA 2020., 9248386, Proceedings of the 15th IEEE Conference on Industrial Electronics and Applications, ICIEA 2020, Institute of Electrical and Electronics Engineers Inc., pp. 1561-1565, 15th IEEE Conference on Industrial Electronics and Applications, ICIEA 2020, Virtual, Kristiansand, Norway, 9/11/20. https://doi.org/10.1109/ICIEA48937.2020.9248386

Autocorrelation Convolution Networks Based on Deep Learning for Automatic Modulation Classification. / Zhang, Duona; Ding, Wenrui; Wang, Hongyu et al.
Proceedings of the 15th IEEE Conference on Industrial Electronics and Applications, ICIEA 2020. Institute of Electrical and Electronics Engineers Inc., 2020. p. 1561-1565 9248386 (Proceedings of the 15th IEEE Conference on Industrial Electronics and Applications, ICIEA 2020).

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

TY - GEN

T1 - Autocorrelation Convolution Networks Based on Deep Learning for Automatic Modulation Classification

AU - Zhang, Duona

AU - Ding, Wenrui

AU - Wang, Hongyu

AU - Zhang, Baochang

PY - 2020/11/9

Y1 - 2020/11/9

N2 - Automatic modulation classification (AMC) is challenging but significant in the field of cognitive radio. Despite recent deep learning methods have dominated as the best performers for AMC, they are challenged by the practical problem in low signal-to-noise ratios (SNRs). In this paper, we propose novel autocorrelation convolution networks (ACNs) to capture periodic representation for communication signals. In ACNs, modulation modes are classified with the periodic local features under an autocorrelation convolution criterion. The experimental results demonstrate that ACNs achieve a great improvement that outperforms recent deep learning methods in low SNRs.

AB - Automatic modulation classification (AMC) is challenging but significant in the field of cognitive radio. Despite recent deep learning methods have dominated as the best performers for AMC, they are challenged by the practical problem in low signal-to-noise ratios (SNRs). In this paper, we propose novel autocorrelation convolution networks (ACNs) to capture periodic representation for communication signals. In ACNs, modulation modes are classified with the periodic local features under an autocorrelation convolution criterion. The experimental results demonstrate that ACNs achieve a great improvement that outperforms recent deep learning methods in low SNRs.

KW - cognitive radio

KW - Deep learning

KW - modulation classification

KW - wireless communication

UR - https://www.scopus.com/pages/publications/85097537459

U2 - 10.1109/ICIEA48937.2020.9248386

DO - 10.1109/ICIEA48937.2020.9248386

M3 - 会议稿件

AN - SCOPUS:85097537459

T3 - Proceedings of the 15th IEEE Conference on Industrial Electronics and Applications, ICIEA 2020

SP - 1561

EP - 1565

BT - Proceedings of the 15th IEEE Conference on Industrial Electronics and Applications, ICIEA 2020

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 15th IEEE Conference on Industrial Electronics and Applications, ICIEA 2020

Y2 - 9 November 2020 through 13 November 2020

ER -

Zhang D, Ding W, Wang H, Zhang B. Autocorrelation Convolution Networks Based on Deep Learning for Automatic Modulation Classification. In Proceedings of the 15th IEEE Conference on Industrial Electronics and Applications, ICIEA 2020. Institute of Electrical and Electronics Engineers Inc. 2020. p. 1561-1565. 9248386. (Proceedings of the 15th IEEE Conference on Industrial Electronics and Applications, ICIEA 2020). doi: 10.1109/ICIEA48937.2020.9248386

Autocorrelation Convolution Networks Based on Deep Learning for Automatic Modulation Classification

Abstract

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Keywords

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