Blind Modulation Recognition in Complex Electromagnetic Environment

摘要

With the continuous development of wireless communication technology, the wireless electromagnetic environment is increasingly complex, which results in the difficulty of modulation recognition of communication signals. In this paper, combining the advantages of ResNet and DenseNet, we propose a blind modulation recognition model based on deep learning. In this model, we reduce the two-dimensional convolution neural network in ResNet into the one-dimensional convolution neural network and then embed it into DenseNet. The identity mapping of ResNet and the dense connection of DenseNet, which strengthen feature propagation and encourage feature reuse and reduce the number of parameters, make the model take full advantage of multi-layer features to improve the ability of feature extraction and reduce the computational complexity. The experimental results on the RadioML2016.10b dataset show that the recognition accuracy of the model can reach 93.7% at high SNR.