基于轻量化深度学习网络的调制信号识别模型

电磁态势分析是信息化战争中至关重要的工作,如何利用深度学习技术有效实现调制信号识别是其中一项关键技术.首先将调制信号转化为带有颜色信息的星座图形式,并用深度学习方法,选用VGG16和AlexNet两个卷积神经网络完成调制识别任务.结果显示,当信噪比大于等于0 dB时,可以达到99％以上的识别准确率.由于军用设备对于计算...

Modulation signal recognition model based on lightweight Deep Learning network

Electromagnetic situational analysis is a crucial task in information warfare, and modulation signal recognition by using Deep Learning(DL) is one of the key technologies. In this paper, the modulation signals are firstly transformed into the form of constellation diagrams with color information, and two Convolutional Neural Networks(CNNs), VGG16 and AlexNet, are selected to complete the modulation signal recognition task by using DL. The results show that a recognition accuracy higher than 99% can be achieved when the Signal-to-Noise Ratio(SNR) of noise is greater than or equal to 0 dB. Since the computational performance and storage performance of military devices are more stringent in controlling, the Average Percentage of Zeroes(APoZ) method is adopted to compress the DL model. The results show that with 0 dB SNR, AlexNet can be compressed by 3 466 times and VGG16 can be compressed by 20 156 times for model parametric quantities, and by 2 314 times and 13 475 times for floating-point operations, respectively, without losing recognition accuracy. In summary, the proposed method is both feasible and efficient in modulation signal recognition.