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A Graph-Based Semi-Supervised Approach for Few-Shot Class-Incremental Modulation Classification 期刊论文
CHINA COMMUNICATIONS, 2024, 页码: 16
Authors:  Zhou, Xiaoyu;  Qi, Peihan;  Liu, Qi;  Ding, Yuanlei;  Zheng, Shilian;  Li, Zan
Favorite  |  View/Download：6/0  |  Submit date：2024/05/23
deep learning  few-shot  label propaga- tion  modulation classification  semi-supervised learn- ing  
Deep Learning Aided Multi-Level Transmit Power Recognition in Cognitive Radio Networks 期刊论文
IEEE TRANSACTIONS ON COGNITIVE COMMUNICATIONS AND NETWORKING, 2023, 卷号: 9, 期号: 2, 页码: 332-344
Authors:  Tan, Zhenyu;  Wang, Danyang;  Liu, Qi;  Li, Zan;  Zhang, Ning;  Abdel-Raheem, Esam
Adobe PDF(3048Kb)  |  Favorite  |  View/Download：10/1  |  Submit date：2023/11/21
Sensors  Feature extraction  Computer architecture  Interference  Deep learning  Convolutional neural networks  Uncertainty  Cognitive radio  convolutional neural network  multiple transmit power levels recognition  
CNN-Enabled Multiple Power-Levels Identification in Cognitive Radio Networks 会议论文
IEEE Global Communications Conference (GLOBECOM), 04-08 December 2022, Rio de Janeiro, BRAZIL, Dec 04-08, 2022
Authors:  Tan, Zhenyu;  Liu, Qi;  Li, Zan;  Wang, Danyang;  Zhang, Ning;  Dai, Hong-Ning
Adobe PDF(925Kb)  |  Favorite  |  View/Download：2/0  |  Submit date：2024/07/02
cognitive radio  convolutional neural network  multiple transmit power  levels identification  non-gaussian signal