A Kind of Wireless Modulation Recognition Method Based on DenseNet and BLSTM
Xiaosong Xie, Guangsong Yang, Mengxi Jiang, Qiubo Ye, Chen-Fu Yang · IEEE Access · 2021
Deep learning has achieved remarkable results in various fields, such as image recognition and classification. However, in the recognition of radio modulation methods, deep learning for different modulation methods of radio signal recognition results are not satisfactory. In this paper, we propose to use densely connected convolutional networks combined with bidirectional recurrent neural networks to identify the radios of 11 different modulation methods. The final results show that our method is more accurate than the traditional convolution neural network in modulation recognition.