3 papers
eess.SP2026
Automatic Modulation Classification via Green Machine Learning
Chee-An Yu, Young-Kai Chen, C. -C. Jay Kuo
In this work, we propose an interpretable, robust, and lightweight machine learning method for automatic modulation classification (AMC) under dynamic and noisy channel conditions.…
eess.SP2026
G-AMC: A Green Automatic Modulation Classification Method
Chee-An Yu, Young-Kai Chen, C. -C. Jay Kuo
In this work, we propose an efficient and transparent green learning pipeline to address the automatic modulation classification (AMC) problem. This pipeline aims to enable receive…
eess.IV2023
Enhancing Edge Intelligence with Highly Discriminant LNT Features
Xinyu Wang, Vinod K. Mishra, C. -C. Jay Kuo
AI algorithms at the edge demand smaller model sizes and lower computational complexity. To achieve these objectives, we adopt a green learning (GL) paradigm rather than the deep l…