Noise-Robust Feature Combination Method for Modulation Classification under Fading Channels

Siyang Zhou; Zhilu Wu; Zhendong Yin; Zhutian Yang

doi:10.1109/VTCFall.2018.8690662

Noise-Robust Feature Combination Method for Modulation Classification under Fading Channels

School of Electronics and Information Engineering, Harbin Institute of Technology

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

6 Scopus citations

Abstract

Automatic modulation classification (AMC) plays an important role in cognitive radio and is widely studied recent years. However, most existing AMC schemes must be deployed under their training SNRs, which makes them highly dependent on the accuracy of channel estimation. The classifiers may need to be re-trained to fit the varying channel condition. To address this problem, a feature combination method aiming to find noise-robust features under fading channels is proposed in this paper. Stacked auto encoder is deployed to explore robust features from an extracted feature set, and these new features is then used to train a support vector machine (SVM). Numerical results shows that the generalization ability of SVMs trained with new features can be significantly improved; therefore the method is robust to SNR variation.

Original language	English
Title of host publication	2018 IEEE 88th Vehicular Technology Conference, VTC-Fall 2018 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781538663585
DOIs	https://doi.org/10.1109/VTCFall.2018.8690662
State	Published - 2 Jul 2018
Externally published	Yes
Event	88th IEEE Vehicular Technology Conference, VTC-Fall 2018 - Chicago, United States Duration: 27 Aug 2018 → 30 Aug 2018

Publication series

Name	IEEE Vehicular Technology Conference
Volume	2018-August
ISSN (Print)	1550-2252

Conference

Conference	88th IEEE Vehicular Technology Conference, VTC-Fall 2018
Country/Territory	United States
City	Chicago
Period	27/08/18 → 30/08/18

Keywords

automatic modulation classification
feature combination
feature extraction
stacked auto-encoder

Access to Document

10.1109/VTCFall.2018.8690662

Cite this

Zhou, S., Wu, Z., Yin, Z., & Yang, Z. (2018). Noise-Robust Feature Combination Method for Modulation Classification under Fading Channels. In 2018 IEEE 88th Vehicular Technology Conference, VTC-Fall 2018 - Proceedings Article 8690662 (IEEE Vehicular Technology Conference; Vol. 2018-August). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/VTCFall.2018.8690662

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title = "Noise-Robust Feature Combination Method for Modulation Classification under Fading Channels",

abstract = "Automatic modulation classification (AMC) plays an important role in cognitive radio and is widely studied recent years. However, most existing AMC schemes must be deployed under their training SNRs, which makes them highly dependent on the accuracy of channel estimation. The classifiers may need to be re-trained to fit the varying channel condition. To address this problem, a feature combination method aiming to find noise-robust features under fading channels is proposed in this paper. Stacked auto encoder is deployed to explore robust features from an extracted feature set, and these new features is then used to train a support vector machine (SVM). Numerical results shows that the generalization ability of SVMs trained with new features can be significantly improved; therefore the method is robust to SNR variation.",

keywords = "automatic modulation classification, feature combination, feature extraction, stacked auto-encoder",

author = "Siyang Zhou and Zhilu Wu and Zhendong Yin and Zhutian Yang",

note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 88th IEEE Vehicular Technology Conference, VTC-Fall 2018 ; Conference date: 27-08-2018 Through 30-08-2018",

year = "2018",

month = jul,

day = "2",

doi = "10.1109/VTCFall.2018.8690662",

language = "英语",

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Zhou, S, Wu, Z , Yin, Z & Yang, Z 2018, Noise-Robust Feature Combination Method for Modulation Classification under Fading Channels. in 2018 IEEE 88th Vehicular Technology Conference, VTC-Fall 2018 - Proceedings., 8690662, IEEE Vehicular Technology Conference, vol. 2018-August, Institute of Electrical and Electronics Engineers Inc., 88th IEEE Vehicular Technology Conference, VTC-Fall 2018, Chicago, United States, 27/08/18. https://doi.org/10.1109/VTCFall.2018.8690662

Noise-Robust Feature Combination Method for Modulation Classification under Fading Channels. / Zhou, Siyang; Wu, Zhilu ; Yin, Zhendong et al.
2018 IEEE 88th Vehicular Technology Conference, VTC-Fall 2018 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2018. 8690662 (IEEE Vehicular Technology Conference; Vol. 2018-August).

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

TY - GEN

T1 - Noise-Robust Feature Combination Method for Modulation Classification under Fading Channels

AU - Zhou, Siyang

AU - Wu, Zhilu

AU - Yin, Zhendong

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Y1 - 2018/7/2

N2 - Automatic modulation classification (AMC) plays an important role in cognitive radio and is widely studied recent years. However, most existing AMC schemes must be deployed under their training SNRs, which makes them highly dependent on the accuracy of channel estimation. The classifiers may need to be re-trained to fit the varying channel condition. To address this problem, a feature combination method aiming to find noise-robust features under fading channels is proposed in this paper. Stacked auto encoder is deployed to explore robust features from an extracted feature set, and these new features is then used to train a support vector machine (SVM). Numerical results shows that the generalization ability of SVMs trained with new features can be significantly improved; therefore the method is robust to SNR variation.

AB - Automatic modulation classification (AMC) plays an important role in cognitive radio and is widely studied recent years. However, most existing AMC schemes must be deployed under their training SNRs, which makes them highly dependent on the accuracy of channel estimation. The classifiers may need to be re-trained to fit the varying channel condition. To address this problem, a feature combination method aiming to find noise-robust features under fading channels is proposed in this paper. Stacked auto encoder is deployed to explore robust features from an extracted feature set, and these new features is then used to train a support vector machine (SVM). Numerical results shows that the generalization ability of SVMs trained with new features can be significantly improved; therefore the method is robust to SNR variation.

KW - automatic modulation classification

KW - feature combination

KW - feature extraction

KW - stacked auto-encoder

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Noise-Robust Feature Combination Method for Modulation Classification under Fading Channels

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