4 papers
Improving the Convergence Rate of Ray Search Optimization for Query-Efficient Hard-Label Attacks
Xinjie Xu, Shuyu Cheng, Dongwei Xu +2
In hard-label black-box adversarial attacks, where only the top-1 predicted label is accessible, the prohibitive query complexity poses a major obstacle to practical deployment. In…
\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…