Radio Signal Automatic Modulation Classification based on Deep Learning and Expert Features

Tianyao Yao; Yuan Chai; Shuai Wang; Xiaqing Miao; Xiangyuan Bu

doi:10.1109/ITNEC48623.2020.9085077

Radio Signal Automatic Modulation Classification based on Deep Learning and Expert Features

Tianyao Yao
, Yuan Chai
, Shuai Wang
, Xiaqing Miao
, Xiangyuan Bu

Beijing Institute of Technology
China Aerospace Science and Technology Corporation

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

18 Citations (Scopus)

Abstract

Automatic modulation classification (AMC) becomes more and more important in the electronic reconnaissance. Recently, lots of researchers focus on deep learning architecture based AMC approach but the recognition rate of WBFM and QAM is less than desirable. In this paper, we proposed a joint AMC model of two expert features and CNN-LSTM networks. Before entering the deep learning network, the un-classified signal is first detected whether WBFM or not by the maximum of zero-center normalization amplitude spectrum density. Then the signal which is not WBFM will be inputted to the CNN-LSTM network, while QAM16 and QAM64 are regarded as the same class here. Finally, Haar-wavelet transform crest searching is used to classify QAM16 and QAM64. Compared with former CNN-LSTM architecture, the results of the experiment and deduction show the average recognition rate of the proposed model is increased by 11.5% at 10 dB SNR.

Original language	English
Title of host publication	Proceedings of 2020 IEEE 4th Information Technology, Networking, Electronic and Automation Control Conference, ITNEC 2020
Editors	Bing Xu, Kefen Mou
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1225-1230
Number of pages	6
ISBN (Electronic)	9781728143903
DOIs	https://doi.org/10.1109/ITNEC48623.2020.9085077
Publication status	Published - Jun 2020
Event	4th IEEE Information Technology, Networking, Electronic and Automation Control Conference, ITNEC 2020 - Chongqing, China Duration: 12 Jun 2020 → 14 Jun 2020

Publication series

Name	Proceedings of 2020 IEEE 4th Information Technology, Networking, Electronic and Automation Control Conference, ITNEC 2020

Conference

Conference	4th IEEE Information Technology, Networking, Electronic and Automation Control Conference, ITNEC 2020
Country/Territory	China
City	Chongqing
Period	12/06/20 → 14/06/20

UN SDGs

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

SDG 7 Affordable and Clean Energy

Keywords

CNN
LSTM
automatic modulation classification
electronic reconnaissance
expert feature

Access to Document

10.1109/ITNEC48623.2020.9085077

Cite this

Yao, T., Chai, Y., Wang, S., Miao, X., & Bu, X. (2020). Radio Signal Automatic Modulation Classification based on Deep Learning and Expert Features. In B. Xu, & K. Mou (Eds.), Proceedings of 2020 IEEE 4th Information Technology, Networking, Electronic and Automation Control Conference, ITNEC 2020 (pp. 1225-1230). Article 9085077 (Proceedings of 2020 IEEE 4th Information Technology, Networking, Electronic and Automation Control Conference, ITNEC 2020). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ITNEC48623.2020.9085077

Yao, Tianyao ; Chai, Yuan ; Wang, Shuai et al. / Radio Signal Automatic Modulation Classification based on Deep Learning and Expert Features. Proceedings of 2020 IEEE 4th Information Technology, Networking, Electronic and Automation Control Conference, ITNEC 2020. editor / Bing Xu ; Kefen Mou. Institute of Electrical and Electronics Engineers Inc., 2020. pp. 1225-1230 (Proceedings of 2020 IEEE 4th Information Technology, Networking, Electronic and Automation Control Conference, ITNEC 2020).

@inproceedings{f5bbbfb135b2413e8fa10410d0403082,

title = "Radio Signal Automatic Modulation Classification based on Deep Learning and Expert Features",

abstract = "Automatic modulation classification (AMC) becomes more and more important in the electronic reconnaissance. Recently, lots of researchers focus on deep learning architecture based AMC approach but the recognition rate of WBFM and QAM is less than desirable. In this paper, we proposed a joint AMC model of two expert features and CNN-LSTM networks. Before entering the deep learning network, the un-classified signal is first detected whether WBFM or not by the maximum of zero-center normalization amplitude spectrum density. Then the signal which is not WBFM will be inputted to the CNN-LSTM network, while QAM16 and QAM64 are regarded as the same class here. Finally, Haar-wavelet transform crest searching is used to classify QAM16 and QAM64. Compared with former CNN-LSTM architecture, the results of the experiment and deduction show the average recognition rate of the proposed model is increased by 11.5\% at 10 dB SNR.",

keywords = "CNN, LSTM, automatic modulation classification, electronic reconnaissance, expert feature",

author = "Tianyao Yao and Yuan Chai and Shuai Wang and Xiaqing Miao and Xiangyuan Bu",

note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 4th IEEE Information Technology, Networking, Electronic and Automation Control Conference, ITNEC 2020 ; Conference date: 12-06-2020 Through 14-06-2020",

year = "2020",

month = jun,

doi = "10.1109/ITNEC48623.2020.9085077",

language = "English",

series = "Proceedings of 2020 IEEE 4th Information Technology, Networking, Electronic and Automation Control Conference, ITNEC 2020",

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

pages = "1225--1230",

editor = "Bing Xu and Kefen Mou",

booktitle = "Proceedings of 2020 IEEE 4th Information Technology, Networking, Electronic and Automation Control Conference, ITNEC 2020",

address = "United States",

}

Yao, T, Chai, Y, Wang, S , Miao, X & Bu, X 2020, Radio Signal Automatic Modulation Classification based on Deep Learning and Expert Features. in B Xu & K Mou (eds), Proceedings of 2020 IEEE 4th Information Technology, Networking, Electronic and Automation Control Conference, ITNEC 2020., 9085077, Proceedings of 2020 IEEE 4th Information Technology, Networking, Electronic and Automation Control Conference, ITNEC 2020, Institute of Electrical and Electronics Engineers Inc., pp. 1225-1230, 4th IEEE Information Technology, Networking, Electronic and Automation Control Conference, ITNEC 2020, Chongqing, China, 12/06/20. https://doi.org/10.1109/ITNEC48623.2020.9085077

Radio Signal Automatic Modulation Classification based on Deep Learning and Expert Features. / Yao, Tianyao; Chai, Yuan; Wang, Shuai et al.
Proceedings of 2020 IEEE 4th Information Technology, Networking, Electronic and Automation Control Conference, ITNEC 2020. ed. / Bing Xu; Kefen Mou. Institute of Electrical and Electronics Engineers Inc., 2020. p. 1225-1230 9085077 (Proceedings of 2020 IEEE 4th Information Technology, Networking, Electronic and Automation Control Conference, ITNEC 2020).

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

TY - GEN

T1 - Radio Signal Automatic Modulation Classification based on Deep Learning and Expert Features

AU - Yao, Tianyao

AU - Chai, Yuan

AU - Wang, Shuai

AU - Miao, Xiaqing

AU - Bu, Xiangyuan

PY - 2020/6

Y1 - 2020/6

N2 - Automatic modulation classification (AMC) becomes more and more important in the electronic reconnaissance. Recently, lots of researchers focus on deep learning architecture based AMC approach but the recognition rate of WBFM and QAM is less than desirable. In this paper, we proposed a joint AMC model of two expert features and CNN-LSTM networks. Before entering the deep learning network, the un-classified signal is first detected whether WBFM or not by the maximum of zero-center normalization amplitude spectrum density. Then the signal which is not WBFM will be inputted to the CNN-LSTM network, while QAM16 and QAM64 are regarded as the same class here. Finally, Haar-wavelet transform crest searching is used to classify QAM16 and QAM64. Compared with former CNN-LSTM architecture, the results of the experiment and deduction show the average recognition rate of the proposed model is increased by 11.5% at 10 dB SNR.

AB - Automatic modulation classification (AMC) becomes more and more important in the electronic reconnaissance. Recently, lots of researchers focus on deep learning architecture based AMC approach but the recognition rate of WBFM and QAM is less than desirable. In this paper, we proposed a joint AMC model of two expert features and CNN-LSTM networks. Before entering the deep learning network, the un-classified signal is first detected whether WBFM or not by the maximum of zero-center normalization amplitude spectrum density. Then the signal which is not WBFM will be inputted to the CNN-LSTM network, while QAM16 and QAM64 are regarded as the same class here. Finally, Haar-wavelet transform crest searching is used to classify QAM16 and QAM64. Compared with former CNN-LSTM architecture, the results of the experiment and deduction show the average recognition rate of the proposed model is increased by 11.5% at 10 dB SNR.

KW - CNN

KW - LSTM

KW - automatic modulation classification

KW - electronic reconnaissance

KW - expert feature

UR - https://www.scopus.com/pages/publications/85086273816

U2 - 10.1109/ITNEC48623.2020.9085077

DO - 10.1109/ITNEC48623.2020.9085077

M3 - Conference contribution

AN - SCOPUS:85086273816

T3 - Proceedings of 2020 IEEE 4th Information Technology, Networking, Electronic and Automation Control Conference, ITNEC 2020

SP - 1225

EP - 1230

BT - Proceedings of 2020 IEEE 4th Information Technology, Networking, Electronic and Automation Control Conference, ITNEC 2020

A2 - Xu, Bing

A2 - Mou, Kefen

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 4th IEEE Information Technology, Networking, Electronic and Automation Control Conference, ITNEC 2020

Y2 - 12 June 2020 through 14 June 2020

ER -

Yao T, Chai Y, Wang S , Miao X, Bu X. Radio Signal Automatic Modulation Classification based on Deep Learning and Expert Features. In Xu B, Mou K, editors, Proceedings of 2020 IEEE 4th Information Technology, Networking, Electronic and Automation Control Conference, ITNEC 2020. Institute of Electrical and Electronics Engineers Inc. 2020. p. 1225-1230. 9085077. (Proceedings of 2020 IEEE 4th Information Technology, Networking, Electronic and Automation Control Conference, ITNEC 2020). doi: 10.1109/ITNEC48623.2020.9085077

Radio Signal Automatic Modulation Classification based on Deep Learning and Expert Features

Abstract

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UN SDGs

Keywords

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