| Digital modulation classification using multi-layer perceptron and time-frequency features |
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| 作者单位： | Yuan Ye（Dept. of Industrial Design and Information Engineering, Beijing Institute of Clothing Technology,Beijing 100029, P. R. China） ;
Mei Wenbo（Dept. of Electronic Engineering, Beijing Inst. of Technology, Beijing 100081, P. R. China） ; |
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| 摘 要： | Considering that real communication signals corrupted by noise are generally nonstationary, and time-frequency distributions are especially suitable for the analysis of nonstationary signals, time-frequency distributions are introduced for the modulation classification of communication signals. The extracted time-frequency features have good classification information, and they are insensitive to signal to noise ratio (SNR) variation. According to good classification by the correct rate of a neural network classifier, a multilayer perceptron (MLP) classifier with better generalization, as well as, addition of time-frequency features set for classifying six different modulation types has been proposed. Computer simulations show that the MLP classifier outperforms the decision-theoretic classifier at low SNRs, and the classification experiments for real MPSK signals verify engineering significance of the MLP classifier.
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| 收稿时间： | 10 December 2006 |
Digital modulation classification using multi-layer perceptron and time-frequency features |
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| Authors: | Yuan Ye Mei Wenbo |
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| Institution: | 1. Dept. of Industrial Design and Information Engineering, Beijing Institute of Clothing Technology,Beijing 100029, P. R. China
2. Dept. of Electronic Engineering, Beijing Inst. of Technology, Beijing 100081, P. R. China |
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| Abstract: | Considering that real communication signals corrupted by noise are generally nonstationary, and time-frequency distributions are especially suitable for the analysis of nonstationary signals, time-frequency distributions are introduced for the modulation classification of communication signals. The extracted time-frequency features have good classification information, and they are insensitive to signal to noise ratio (SNR) variation. According to good classification by the correct rate of a neural network classifier, a multilayer perceptron (MLP) classifier with better generalization, as well as, addition of time-frequency features set for classifying six different modulation types has been proposed. Computer simulations show that the MLP classifier outperforms the decision-theoretic classifier at low SNRs, and the classification experiments for real MPSK signals verify engineering significance of the MLP classifier. |
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| Keywords: | Digital modulation classification Time-frequency feature Time-frequency distribution Multi-layer perceptron |
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