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中文摘要：
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Na—kagami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。
英文摘要：
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 eonstructsed 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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