Co-channel multi-signal modulation classification based on convolution neural network

Zhendong Yin; Rui Zhang; Zhilu Wu; Xiaojun Zhang

doi:10.1109/VTCSpring.2019.8746292

Co-channel multi-signal modulation classification based on convolution neural network

Zhendong Yin
, Rui Zhang
, Zhilu Wu
, Xiaojun Zhang

School of Electronics and Information Engineering, Harbin Institute of Technology

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

14 Scopus citations

Abstract

The research for co-channel multi-signal modulation classification has become urgent with the increasing shortage of spectral bandwidth. Single-signal modulation classification methods which have been widely studied are not applicable for co-channel multi- signal modulation classification problem. In this paper, we developed a method for co-channel multi- signal modulation classification based on Convolution Neural Network(CNN). The proposed method can identify 31 mixed signals from 5 modulation types. The proposed method are also found to be robust to the changes of SNR from 0dB to 15dB. The experiments are performed to prove the effectiveness of the proposed method.

Original language	English
Title of host publication	2019 IEEE 89th Vehicular Technology Conference, VTC Spring 2019 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781728112176
DOIs	https://doi.org/10.1109/VTCSpring.2019.8746292
State	Published - Apr 2019
Externally published	Yes
Event	89th IEEE Vehicular Technology Conference, VTC Spring 2019 - Kuala Lumpur, Malaysia Duration: 28 Apr 2019 → 1 May 2019

Publication series

Name	IEEE Vehicular Technology Conference
Volume	2019-April
ISSN (Print)	1550-2252

Conference

Conference	89th IEEE Vehicular Technology Conference, VTC Spring 2019
Country/Territory	Malaysia
City	Kuala Lumpur
Period	28/04/19 → 1/05/19

Access to Document

10.1109/VTCSpring.2019.8746292

Cite this

Yin, Z., Zhang, R., Wu, Z., & Zhang, X. (2019). Co-channel multi-signal modulation classification based on convolution neural network. In 2019 IEEE 89th Vehicular Technology Conference, VTC Spring 2019 - Proceedings Article 8746292 (IEEE Vehicular Technology Conference; Vol. 2019-April). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/VTCSpring.2019.8746292

@inproceedings{04b0f0ee4f7c40f7a69fd5d613a75456,

title = "Co-channel multi-signal modulation classification based on convolution neural network",

abstract = "The research for co-channel multi-signal modulation classification has become urgent with the increasing shortage of spectral bandwidth. Single-signal modulation classification methods which have been widely studied are not applicable for co-channel multi- signal modulation classification problem. In this paper, we developed a method for co-channel multi- signal modulation classification based on Convolution Neural Network(CNN). The proposed method can identify 31 mixed signals from 5 modulation types. The proposed method are also found to be robust to the changes of SNR from 0dB to 15dB. The experiments are performed to prove the effectiveness of the proposed method.",

author = "Zhendong Yin and Rui Zhang and Zhilu Wu and Xiaojun Zhang",

note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 89th IEEE Vehicular Technology Conference, VTC Spring 2019 ; Conference date: 28-04-2019 Through 01-05-2019",

year = "2019",

month = apr,

doi = "10.1109/VTCSpring.2019.8746292",

language = "英语",

series = "IEEE Vehicular Technology Conference",

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

booktitle = "2019 IEEE 89th Vehicular Technology Conference, VTC Spring 2019 - Proceedings",

address = "美国",

}

Yin, Z, Zhang, R, Wu, Z & Zhang, X 2019, Co-channel multi-signal modulation classification based on convolution neural network. in 2019 IEEE 89th Vehicular Technology Conference, VTC Spring 2019 - Proceedings., 8746292, IEEE Vehicular Technology Conference, vol. 2019-April, Institute of Electrical and Electronics Engineers Inc., 89th IEEE Vehicular Technology Conference, VTC Spring 2019, Kuala Lumpur, Malaysia, 28/04/19. https://doi.org/10.1109/VTCSpring.2019.8746292

Co-channel multi-signal modulation classification based on convolution neural network. / Yin, Zhendong; Zhang, Rui; Wu, Zhilu et al.
2019 IEEE 89th Vehicular Technology Conference, VTC Spring 2019 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2019. 8746292 (IEEE Vehicular Technology Conference; Vol. 2019-April).

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

TY - GEN

T1 - Co-channel multi-signal modulation classification based on convolution neural network

AU - Yin, Zhendong

AU - Zhang, Rui

AU - Wu, Zhilu

AU - Zhang, Xiaojun

PY - 2019/4

Y1 - 2019/4

N2 - The research for co-channel multi-signal modulation classification has become urgent with the increasing shortage of spectral bandwidth. Single-signal modulation classification methods which have been widely studied are not applicable for co-channel multi- signal modulation classification problem. In this paper, we developed a method for co-channel multi- signal modulation classification based on Convolution Neural Network(CNN). The proposed method can identify 31 mixed signals from 5 modulation types. The proposed method are also found to be robust to the changes of SNR from 0dB to 15dB. The experiments are performed to prove the effectiveness of the proposed method.

AB - The research for co-channel multi-signal modulation classification has become urgent with the increasing shortage of spectral bandwidth. Single-signal modulation classification methods which have been widely studied are not applicable for co-channel multi- signal modulation classification problem. In this paper, we developed a method for co-channel multi- signal modulation classification based on Convolution Neural Network(CNN). The proposed method can identify 31 mixed signals from 5 modulation types. The proposed method are also found to be robust to the changes of SNR from 0dB to 15dB. The experiments are performed to prove the effectiveness of the proposed method.

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DO - 10.1109/VTCSpring.2019.8746292

M3 - 会议稿件

AN - SCOPUS:85068987228

T3 - IEEE Vehicular Technology Conference

BT - 2019 IEEE 89th Vehicular Technology Conference, VTC Spring 2019 - Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 89th IEEE Vehicular Technology Conference, VTC Spring 2019

Y2 - 28 April 2019 through 1 May 2019

ER -

Co-channel multi-signal modulation classification based on convolution neural network

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