Adaptive Multi-Dimensional Shrinkage Block for Automatic Modulation Recognition

Tao Wei,Zan Li, Dexin Bi, Zixuan Shao,Jingliang Gao

IEEE COMMUNICATIONS LETTERS(2023)

引用 0|浏览4
暂无评分
摘要
Low Signal-to-Noise Ratio (SNR) conditions pose significant challenges in Automatic Modulation Recognition (AMR) tasks. In this letter, we propose an innovative Multi-Dimensional Shrinkage Block (MDSB) to address these challenges. MDSB is a novel Convolutional Neural Network (CNN) architecture that effectively enhances the noise robustness of CNNs by employing a unique denoising mechanism, which tackles the limitations of CNNs in extracting temporal information. Leveraging the MDSB, a new AMR network named the Spatial and Channel-wise Shrinkage Neural Network (SCSNN) is introduced. Comprehensive experiments on multiple public datasets demonstrate the superior recognition performance of the proposed SCSNN model in comparison to other methods.
更多
查看译文
关键词
Automatic modulation recognition,noise adaptive reduction,deep learning,soft thresholding
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要