关键词: 复数神经网络, 复杂电磁环境, 调制样式, 相位信息, 调制识别, I/Q数据, 潜在特征, 电磁信号

Abstract:

In the region of complex electromagnetic environment, it is difficult to obtain the signal modulation type. The traditional recognition and classification methods of modulated signals are not successful because of their own defects. The current deep learning methods usually used for signal modulation are based on real values for characterization and processing, which results in recognition bias due to the loss of the original intrinsic connection of complex values. To solve this situation, the complex deep neural network is applied to the modulation recognition of electromagnetic signals, complex convolutional deep neural networks such as complex convolutional deep neural networks, batch normalization and fully connected networks are designed, and the final classification task is completed by softmax function. The standard data set RML2016.10a is used to complete the training as well as testing of the network. The experimental results show that the trained complex deep neural network is significantly better than traditional recognition algorithms, and can effectively improve the recognition rate of electromagnetic signals.

Key words: complex-valued neural network, complex electromagnetic environment, modulation style, phase information, modulation recognition, I/Q data, potential characteristics, electromagnetic signal