基于零样本学习和自编码器的调制信号识别研究

Zero shot learning and autoencoder based modulation signal recognition

针对深度学习模型在信号调制识别应用中无法有效识别未知调制方式的问题,提出了一种基于零样本学习和自编码器的信号调制识别模型,用于信号调制开集识别。通过自编码器提取调制信号的特征,引入交叉熵损失、中心损失和重构损失使得不同调制信号的特征能够良好分离,进一步根据特征空间的分布进行调制信号的开集识别。此外,利用解码器重构信号并加入训练,有效提升了模型识别率。实验结果表明,模型能够在提升已知类识别率的前提下对未知类进行区分,且对未知类的分类效果优于传统的开集识别方法,其中未知类识别率达到80%,已知类识别率稳定在95%左右。

To address the challenge of effectively recognizing unknown modulation types in signal modulation recognition applications using deep learning models,this paper introduces a novel recognition model based on zero-shot learning and autoencoders for open set signal modulation recognition.Features of the modulation signals are extracted through an autoencoder,which incorporates cross-entropy loss,center loss,and reconstruction loss to ensure effective separation of features across different modulation types.Further,open set recognition of modulation signals is conducted based on the distribution of features in the feature space.Additionally,by incorporating the reconstructed signals back into training,the model's recognition accuracy is significantly enhanced.Experimental results demonstrate that the proposed model not only distinguishes unknown classes effectively,achieving an unknown class recognition rate of 80%,but also maintains a stable known class recognition rate of approximately 95%,outperforming traditional open set recognition methods.