基于卷积神经网络和稀疏滤波的调制识别方法

针对当前通信系统所采用的主要调制方式,提出了一种基于卷积神经网络和稀疏滤波的调制识别方法。首先,分析了利用信号循环谱二维灰度图进行通信信号调制识别的可行性;然后,通过降采样和裁剪技术对循环谱图预处理;最后,设计了深度卷积神经网络架构,并提出了稀疏滤波预训练的方法。仿真结果表明:相比于经典的基于深度学习的调制识别方法,该方法模型简单,优化量少,且在小样本场景下性能最佳,具有很高应用价值。

Modulation classification based on convolutional neural network and sparse filtering

In consideration of the problem of modulation classification in communication systems, a classification method based on convolutional neural network and sparse filtering is presented. First, the feasibility of modulation classification based on the two dimensional gray images of the cyclic spectrum is analyzed. Then, down-sampling and clipping technologies are used for preprocessing of cyclic spectrum images, and finally the architecture of the deep convolutional neural network is designed and a pre-training procedure based on sparse filtering is proposed. The simulation results show that, compared with the state-of-the-art deep learning-based methods, the proposed method has the advantages of simple model, less optimization and best performance in small sample scenarios, and it also has a good value for practice application.