5 papers · 1 filter
\emph{FoQuS}: A Forgetting-Quality Coreset Selection Framework for Automatic Modulation Recognition
Yao Lu, Chunfeng Sun, Dongwei Xu +3
Deep learning-based Automatic Modulation Recognition (AMR) model has made significant progress with the support of large-scale labeled data. However, when developing new models or…
DUSE: A Data Expansion Framework for Low-resource Automatic Modulation Recognition based on Active Learning
Yao Lu, Hongyu Gao, Zhuangzhi Chen +4
Although deep neural networks have made remarkable achievements in the field of automatic modulation recognition (AMR), these models often require a large amount of labeled data fo…
FCOS: A Two-Stage Recoverable Model Pruning Framework for Automatic Modulation Recognition
Yao Lu, Tengfei Ma, Zeyu Wang +5
With the rapid development of wireless communications and the growing complexity of digital modulation schemes, traditional manual modulation recognition methods struggle to extrac…
MDM: Advancing Multi-Domain Distribution Matching for Automatic Modulation Recognition Dataset Synthesis
Dongwei Xu, Jiajun Chen, Yao Lu +5
Recently, deep learning technology has been successfully introduced into Automatic Modulation Recognition (AMR) tasks. However, the success of deep learning is all attributed to th…
A Generic Layer Pruning Method for Signal Modulation Recognition Deep Learning Models
Yao Lu, Yutao Zhu, Yuqi Li +4
With the successful application of deep learning in communications systems, deep neural networks are becoming the preferred method for signal classification. Although these models…