基于信息熵和GA-ELM的调制识别算法

Modulation recognition algorithm based on information entropy and GA-ELM

针对当前通信信号调制识别算法在低信噪比(signal-to-noise ratio,SNR)下识别率低、训练速度慢、识别调制类型少的问题,提出了基于信息熵特征和遗传算法超限学习机(genetic algorithmextreme learning machine,GA-ELM)的调制识别算法。首先,提取信号的4种熵特征:奇异谱香农熵、奇异谱指数熵、功率谱香农熵和功率谱指数熵作为调制识别的特征参数;其次,采用GA-ELM作为分类器。仿真实验表明,对11种模拟、数字调制信号进行分类识别,在SNR大于4 dB时算法的总体识别率均超过98%,同时该算法训练速度快,识别系统设计简单,具有较大的应用价值。

In order to solve the problems of low recognition rate under low signal-to-noise ratio,slow training speed and few types of modulation in the current modulation recognition algorithms,this paper proposes a modulation recognition algorithm based on entropy feature and genetic algorithm-extreme learning machine(GA-ELM).Firstly,the four entropy characteristics of signals are extracted:Shannon entropy of singular spectrum,index entropy of singular spectrum,Shannon entropy of power spectrum and index entropy of power spectrum.Secondly,GA-ELM is used as the classifier.The simulation results show that the overall recognition rate of this algorithm is over 98%when the signal-to-noise ratio is more than 4 dB.At the same time,the algorithm has fast training speed,simple recognition system design and great application value.