Radio Signals Modulation Mode Recognition Based on Semisupervised Deep Learning

摘要：

　　With the rapid development of radio communication technology,the application requirement for recognizing modulation mode of radio communication has also increased,therefore it has become a research focus.This paper proposes a method by combining semi-supervised ideas with the CNN network to realize radio modulation recognition.The basic idea is to design a deep learning model based on convolutional neural networks,which will make full use of a large number of unlabeled radio signals in air as well as the labeled radio signals to train convolutional neural networks.Our experimental results illustrate that the performance of our proposed method in recognizing BPSK,QPSK,8PSK,4QAM,16QAM,and 64QAM modulation modes in various environments is better than MLP and CNN.

关键词： modulation mode recognition convolutional neural network deep learning semisupervised

作者: Xuezhi He Lin Lin Jinbao Xie

作者单位: Innovation and Development Center,Newland Computer(Fujian)Co.,Ltd.Fuzhou,China Academy of Photoelectric Technology Hefei University of Technology,Hefei,China

会议类型: 国际会议

会议名称: 2018 3rd International Conference on Computer Science and Information Engineering (ICCSIE 2018) 2018第三届计算机科学与信息工程国际会议(ICCSIE 2018)

会议地点: 西安

会议语种:英文

页码: 299-305

在线出版日期: 2018-09-21（万方平台首次上网日期，不代表论文的发表时间）