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

现有的无线电信号调制识别方法在先验数据不足时通常很难对无类标信号进行有效识别。针对这个问题,本文提出了一种基于知识迁移的深度学习无线电信号聚类方法(DTC)。该方法基于样本对比,分析样本间的相似性,并利用卷积神经网络(CNN)提取无线电信号的特征,同时设计了一种预训练框架,通过迁移同领域数据集的知识,有效提升了CNN特征提取能力,实现了引导聚类方向、提升聚类性能的目标。实验结果表明,该方法在多个公开数据集上的聚类性能都显著优于现有的聚类方法。与现有方法相比,DTC在RML 2016.10A和RML 2016.04C数据集上的聚类精度分别提升了30.34%和28.04%。

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.