一种欠完备自编码器调制识别技术

基于信号特征进行模式识别的调制识别方法需要先计算信号的高阶特征、高阶累积量再进行模式识别,整体设计复杂,特征不易计算。机器学习技术由于其强大的特征提取能力和分类能力,被广泛应用到模式识别领域中。针对调制识别问题,提出了一种基于欠完备自编码器的调制识别技术,使用欠完备自编码器进行调制信号的特征自动提取,再使用神经网络分类器进行分类识别。整体模型更为简洁,运算复杂度较低,有利于部署在硬件上进行实时识别。对常见的BPSK、QPSK、2ASK、2FSK、16QAM数字调制方式进行的识别实验表明,算法在信噪比10 dB时平均识别率高于0.97,并且在信噪比为0 dB时仍然有0.92以上的平均识别率。

An Undercomplete Autoencoder Modulation Recognition Technology

In modulation recognition methods based on signal feature,high-order features or high-order cumulants of the modulated signal usually need to be calculated,which is complex in general design and difficult in calculating.Machine learning technology has been widely used in pattern recognition due to its powerful feature extraction and classification capabilities.Therefore,for the problem of modulation recognition,a technology based on undercomplete autoencoder is proposed.Undercomplete autoencoder is used to automatically extract features of modulated signal,and a neural network classifier is used for recognition.The overall model is more concise and has lower computational complexity,which facilitates real-time recognition on the hardware. Experiments show that five common digital modulation methods can be identified by this method,including BPSK,QPSK,2ASK,2FSK,and 16QAM.The average recognition accuracy is higher than 0.97 when the signal-to-noise ratio( SNR) is 10 dB,also the average recognition accuracy can be above 0.92 at 0 dB SNR.