基于最小均方差拟合的QAM调制识别器

Fitting Based on Minimum Mean Square Error QAM Classifier

针对QAM(Quadrature Amplitude Modulation)识别中存在着识别率受信道参数和噪声影响大、低信噪比条件下识别率不高等问题,提出一种基于最小均方差拟合的识别方法。根据不同QAM调制的信号特征,找到了其对应的特征序列,推导了该特征序列的概率密度函数(pdf)和经验累积分布函数(cdf),并对其进行折叠处理,构造了基于最小均方差拟合的折叠cdf识别方法。仿真结果表明,该方法能够在瑞利信道和高斯白噪声下应用,且该方法在低信噪比下识别概率较高,能够应用于电子战,电子干扰和频谱检测等工程领域中。

For QAM(Quadrature Amplitude Modulation)recognition,there is a problem that the recognition rate is affected by the channel parameters and noise,the recognition rate is low under low SNR conditions,and a recognition method based on the minimum mean square error fitting is proposed.According to different QAM modulations,the signal features were found and their corresponding feature sequences were found.The probability density function(pdf)and empirical cumulative distribution function(cdf)of the feature sequence were deduced and folded,and the folding based on the minimum mean square error fitting was constructed.The cdf identification method.The simulation results show that this method can be applied in a Rayleigh channel and Gaussian white noise,and the method has high recognition probability under low signal to SNR.It can be applicable to electronic warfare,electronic jamming and spectrum detection and other engineering fields.