| A novel adaptive classification scheme for digital modulations in satellite communication |
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| 作者姓名： | 吴丹 Gu Xuemai Guo Qing |
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| 作者单位： | Communications Research Center, Harbin Institute of Technology, Harbin 150001, P.R.China t |
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| 基金项目： | 国家高技术研究发展计划(863计划) |
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| 摘 要： | To make the modulation classification system more suitable for signals in a wide range of signal to noise ratios （SNRs）, a novel adaptive modulation classification scheme is presented in this paper. Differ-ent from traditional schemes, the proposed scheme employs a new SNR estimation algorithm for small samples before modulation classification, which makes the modulation classifier work adaptively according to estimated SNRs. Furthermore, it uses three efficient features and support vector machines （SVM） in modulation classification. Computer simulation shows that the scheme can adaptively classify ten digital modulation types （i.e. 2ASK, 4ASK, 2FSK, 4FSK, 2PSK, 4PSK, 16QAM, TFM, π/4QPSK and OQPSK） at SNRS ranging from 0dB to 25dB and success rates are over 95% when SNR is not lower than 3dB. Accuracy, efficiency and simplicity of the proposed scheme are obviously improved, which make it more adaptive to engineering applications.
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| 关 键 词： | 适应分类系统 卫星通讯 矢量 设计软件 |
A novel adaptive classification scheme for digital modulations in satellite communication |
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| Wu Dan,Gu Xuemai,Guo Qing.A novel adaptive classification scheme for digital modulations in satellite communication[J].High Technology Letters,2007,13(2):145-149. |
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| Authors: | Wu Dan Gu Xuemai Guo Qing |
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| Abstract: | To make the modulation classification system more suitable for signals in a wide range of signal to noise ratios (SNRs) , a novel adaptive modulation classification scheme is presented in this paper. Different from traditional schemes, the proposed scheme employs a new SNR estimation algorithm for small samples before modulation classification, which makes the modulation classifier work adaptively according to estimated SNRs. Furthermore, it uses three efficient features and support vector machines (SVM) in modulation classification. Computer simulation shows that the scheme can adaptively classify ten digital modulation types (i.e. 2ASK, 4ASK, 2FSK, 4FSK, 2PSK, 4PSK, 16QAM, TFM, π/4QPSK and OQPSK) at SNRS ranging from OdB to 25 dB and success rates are over 95% when SNR is not lower than 3dB. Accuracy, efficiency and simplicity of the proposed scheme are obviously improved, which make it more adaptive to engineering applications. |
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| Keywords: | adaptive modulation classification support vector machine SNR estimation digital modulation |
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