Fusion Methods for CNN-Based Automatic Modulation Classification

IEEE Access

Xiaoniu Yang¹  Shilian Zheng¹  Peihan Qi²  Shichuan Chen³ 

[1] Science and Technology on Communication Information Security Control Laboratory, Jiaxing, China;State Key Laboratory of Integrated Service Networks, Xidian University, Xi&x2019;an, China;

关键词: Modulation classification;    deep learning;    fusion;    convolutional neural network;    residual network;    wireless communications;   

DOI  :  10.1109/ACCESS.2019.2918136

来源: DOAJ

【 摘 要 】

An automatic modulation classification has a very broad application in wireless communications. Recently, deep learning has been used to solve this problem and achieved superior performance. In most cases, the input size is fixed in convolutional neural network (CNN)-based modulation classification. However, the duration of the actual radio signal burst is variable. When the signal length is greater than the CNN input length, how to make full use of the complete signal burst to improve the classification accuracy is a problem needs to be considered. In this paper, three fusion methods are proposed to solve this problem, such as voting-based fusion, confidence-based fusion, and feature-based fusion. The simulation experiments are done to analyze the performance of these methods. The results show that the three fusion methods perform better than the non-fusion method. The performance of the two fusion methods based on confidence and feature is very close, which is better than that of the voting-based fusion.

【 授权许可】

Unknown