高技术通讯2023，Vol.33Issue(11)：1172-1180,9.DOI:10.3772/j.issn.1002-0470.2023.11.005
基于知识迁移的深度学习无线电信号聚类方法
A deep learning radio signal clustering method based on knowledge transfer
摘要
Abstract
The existing radio signal modulation identification methods are usually difficult to effectively identify the un-classified signal when the prior data is insufficient.To solve this problem,this paper proposes a deep transfer clus-tering(DTC)of radio signals method based on knowledge transfer.This method analyzes the similarity between samples based on sample comparison,and uses a convolutional neural network(CNN)to extract the features of ra-dio signals.At the same time,a pre-training framework is designed,which effectively improves the feature extrac-tion ability of CNN by transferring the knowledge of the same domain dataset and achieves the goal of guiding the clustering direction and improving the clustering performance.The experimental results show that the clustering per-formance of this method is significantly better than the existing clustering methods on multiple public datasets.Compared with existing methods,the clustering accuracy of DTC on the RML2016.10A and RML2016.04C data-sets is improved by 30.34%and 28.04%,respectively.关键词
信号聚类/深度学习/调制识别/迁移学习/卷积神经网络(CNN)Key words
signal clustering/deep learning/modulation recognition/transfer learning/convolutional neural network(CNN)引用本文复制引用
李晓慧,陈壮志,徐东伟,赵文红,宣琦..基于知识迁移的深度学习无线电信号聚类方法[J].高技术通讯,2023,33(11):1172-1180,9.基金项目
国家自然科学基金(61973273)和浙江省自然科学基金(LR19F030001)资助项目. （61973273）
