衰落信道下基于支持向量机的调制识别方法

龚晓洁, 朱琦

龚晓洁, 朱琦. 衰落信道下基于支持向量机的调制识别方法[J]. 信号处理, 2010, 26(8): 1234-1239.
引用本文: 龚晓洁, 朱琦. 衰落信道下基于支持向量机的调制识别方法[J]. 信号处理, 2010, 26(8): 1234-1239.
GONG Xiao-Jie, ZHU Qi. Modulation Recognition Based on SVM In Fading Channels[J]. JOURNAL OF SIGNAL PROCESSING, 2010, 26(8): 1234-1239.
Citation: GONG Xiao-Jie, ZHU Qi. Modulation Recognition Based on SVM In Fading Channels[J]. JOURNAL OF SIGNAL PROCESSING, 2010, 26(8): 1234-1239.

衰落信道下基于支持向量机的调制识别方法

基金项目: 国家自然科学基金(60772062),国家重点基础研究发展计划资助(2007CB310607),国家科技重大专项
(2009ZX03003-002),东南大学移动通信国家重点实验室开放研究基金资助课题(N200813
详细信息
  • 中图分类号: TN929.5

Modulation Recognition Based on SVM In Fading Channels

  • 摘要: 本文以四阶累积量为特征参数,采用支持向量机(SVM)将分类特征值映射到高维空间中,并构建最优分类超平面,实现对QPSK、16QAM、64QAM和OFDM四种信号的自动调制识别。分析了AWGN信道、Rayleigh衰落信道和Nakagami衰落信道对四阶累积量的影响,推导并给出了经过衰落信道后四阶累积量的表达式。基于支持向量机的调制识别方法解决了特征样本在低维空间的不可分问题,仿真结果表明,在SNR低于10dB时,该方法的性能明显优于决策树方法,信噪比大于等于0dB时,各种信号的调制识别率在90%以上。
    Abstract: In this paper the method of recognizing signals modulated by QPSK, 16QAM, 64QAM and OFDM is presented based on the support vector machine(SVM) algorithm with the characteristic parameter of forth order cumulants. SVM maps the feature values of classification into high dimension space, in which the optimal separating hyperplane is constructsed to realize the separation of the signals with targeted modulation method. The influence of AWGN channel, Rayleigh and Nakagami fading channels on the characteristic parameter is analyzed respectively, and the explicit expression of the characteristic parameter is further derived. The recognition method based on SVM can resolve the nonseparable problem in low dimension space. The simulation results show that the performance of the SVM-based method is much better than that of the decision tree method when SNR is lower than 10dB. With SNR larger than 0db, the successful recognition rates of the four types of signals are all above 90%.
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出版历程
  • 收稿日期:  2009-11-17
  • 修回日期:  2010-03-13
  • 发布日期:  2010-08-24

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