Research on Modulation Recognition Algorithm Based on Channel and Spatial Self-Attention Mechanism

In the harsh electromagnetic environment with strong interference, the prior information of the received signal can not be fully obtained, and considering the complex and variable modulation modes, the modulation recognition of radio signals has brought great trouble. In this paper, we propose a method for automatic modulation recognition based on deep convolutional neural network for channel and spatial self-attention mechanism by combining the architecture of feature autonomous learning of deep learning. The correlation of input vectors in channel and spatial dimensions is enhanced by a self-attentive mechanism, and the number of layers of network structure, connection method, pooling method, and hyper-parameters are optimized, to enhance the overall fitting ability of the network and improve the accuracy and robustness of modulation recognition. On the RadioML2016.10A data set, the proposed method is compared with the previous baseline method at different signal-to-noise ratios. The experimental results show that the performance of this paper's method is better in the identification of 16QAM versus 64QAM.