IEICE TRANSACTIONS on Information
A Robust Signal Recognition Method for Communication System under Time-Varying SNR Environment
Summary :
As a consequence of recent developments in communications, the parameters of communication signals, such as the modulation parameter values, are becoming unstable because of time-varying SNR under electromagnetic conditions. In general, it is difficult to classify target signals that have time-varying parameters using traditional signal recognition methods. To overcome this problem, this study proposes a novel recognition method that works well even for such time-dependent communication signals. This method is mainly composed of feature extraction and classification processes. In the feature extraction stage, we adopt Shannon entropy and index entropy to obtain the stable features of modulated signals. In the classification stage, the interval gray relation theory is employed as suitable for signals with time-varying parameter spaces. The advantage of our method is that it can deal with time-varying SNR situations, which cannot be handled by existing methods. The results from numerical simulation show that the proposed feature extraction algorithm, based on entropy characteristics in time-varying SNR situations,offers accurate clustering performance, and the classifier, based on interval gray relation theory, can achieve a recognition rate of up to 82.9%, even when the SNR varies from -10 to -6 dB.
- Publication
- IEICE TRANSACTIONS on Information Vol.E96-D No.12 pp.2814-2819
- Publication Date
- 2013/12/01
- Publicized
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.E96.D.2814
- Type of Manuscript
- PAPER
- Category
- Pattern Recognition
Authors
Jing-Chao LI
Harbin Engineering University
Yi-Bing LI
Harbin Engineering University
Shouhei KIDERA
The University of Electro-Communications
Tetsuo KIRIMOTO
The University of Electro-Communications
Keyword
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Jing-Chao LI, Yi-Bing LI, Shouhei KIDERA, Tetsuo KIRIMOTO, "A Robust Signal Recognition Method for Communication System under Time-Varying SNR Environment" in IEICE TRANSACTIONS on Information,
vol. E96-D, no. 12, pp. 2814-2819, December 2013, doi: 10.1587/transinf.E96.D.2814.
Abstract: As a consequence of recent developments in communications, the parameters of communication signals, such as the modulation parameter values, are becoming unstable because of time-varying SNR under electromagnetic conditions. In general, it is difficult to classify target signals that have time-varying parameters using traditional signal recognition methods. To overcome this problem, this study proposes a novel recognition method that works well even for such time-dependent communication signals. This method is mainly composed of feature extraction and classification processes. In the feature extraction stage, we adopt Shannon entropy and index entropy to obtain the stable features of modulated signals. In the classification stage, the interval gray relation theory is employed as suitable for signals with time-varying parameter spaces. The advantage of our method is that it can deal with time-varying SNR situations, which cannot be handled by existing methods. The results from numerical simulation show that the proposed feature extraction algorithm, based on entropy characteristics in time-varying SNR situations,offers accurate clustering performance, and the classifier, based on interval gray relation theory, can achieve a recognition rate of up to 82.9%, even when the SNR varies from -10 to -6 dB.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.E96.D.2814/_p
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@ARTICLE{e96-d_12_2814,
author={Jing-Chao LI, Yi-Bing LI, Shouhei KIDERA, Tetsuo KIRIMOTO, },
journal={IEICE TRANSACTIONS on Information},
title={A Robust Signal Recognition Method for Communication System under Time-Varying SNR Environment},
year={2013},
volume={E96-D},
number={12},
pages={2814-2819},
abstract={As a consequence of recent developments in communications, the parameters of communication signals, such as the modulation parameter values, are becoming unstable because of time-varying SNR under electromagnetic conditions. In general, it is difficult to classify target signals that have time-varying parameters using traditional signal recognition methods. To overcome this problem, this study proposes a novel recognition method that works well even for such time-dependent communication signals. This method is mainly composed of feature extraction and classification processes. In the feature extraction stage, we adopt Shannon entropy and index entropy to obtain the stable features of modulated signals. In the classification stage, the interval gray relation theory is employed as suitable for signals with time-varying parameter spaces. The advantage of our method is that it can deal with time-varying SNR situations, which cannot be handled by existing methods. The results from numerical simulation show that the proposed feature extraction algorithm, based on entropy characteristics in time-varying SNR situations,offers accurate clustering performance, and the classifier, based on interval gray relation theory, can achieve a recognition rate of up to 82.9%, even when the SNR varies from -10 to -6 dB.},
keywords={},
doi={10.1587/transinf.E96.D.2814},
ISSN={1745-1361},
month={December},}
Copy
TY - JOUR
TI - A Robust Signal Recognition Method for Communication System under Time-Varying SNR Environment
T2 - IEICE TRANSACTIONS on Information
SP - 2814
EP - 2819
AU - Jing-Chao LI
AU - Yi-Bing LI
AU - Shouhei KIDERA
AU - Tetsuo KIRIMOTO
PY - 2013
DO - 10.1587/transinf.E96.D.2814
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E96-D
IS - 12
JA - IEICE TRANSACTIONS on Information
Y1 - December 2013
AB - As a consequence of recent developments in communications, the parameters of communication signals, such as the modulation parameter values, are becoming unstable because of time-varying SNR under electromagnetic conditions. In general, it is difficult to classify target signals that have time-varying parameters using traditional signal recognition methods. To overcome this problem, this study proposes a novel recognition method that works well even for such time-dependent communication signals. This method is mainly composed of feature extraction and classification processes. In the feature extraction stage, we adopt Shannon entropy and index entropy to obtain the stable features of modulated signals. In the classification stage, the interval gray relation theory is employed as suitable for signals with time-varying parameter spaces. The advantage of our method is that it can deal with time-varying SNR situations, which cannot be handled by existing methods. The results from numerical simulation show that the proposed feature extraction algorithm, based on entropy characteristics in time-varying SNR situations,offers accurate clustering performance, and the classifier, based on interval gray relation theory, can achieve a recognition rate of up to 82.9%, even when the SNR varies from -10 to -6 dB.
ER -
English
