【摘要】 针对瑞利信道中信号调制识别问题,提出一种基于高阶累量的树状分类器。分类器中累量理论值经过推导得到,分类向量的选择基于错误概率最小的原则,采用最大化类间距离的方法。利用该分类器进行识别,不需预先知道信道参数。理论分析和仿真表明,信噪比10dB时,在适当数据长度情况下,各种信号识别概率在95%以上,该算法适合在瑞利信道下分类。更多还原

【Abstract】 A new tree structure classifier based on high order cumulants is proposed for modulation recognition in the Rayleigh flat fading channel.The theoretical values of high order cumulants in classifier are derived in particular.We select the classification characteristic depending on the rule of minimum error probability and use the method of maximum distance among different modulation types.The parameters of channel are not necessary to know for classification.Theoretical analysis and simulation results prove average successful rates are more than 95% for SNR =10dB in Rayleigh channel.更多还原