cellular system, deep learning, signal classification, spectrum awareness, Convolutional Neural Network (CNN)
Radio spectrum awareness, including understanding radio signal activities, is crucial for improving spectrum utilization, detecting security vulnerabilities, and supporting adaptive transmissions. Related tasks include spectrum sensing, identifying systems and terminals, and understanding various protocol layers. In this paper, we investigate various identification and classification tasks related to fading channel parameters, signal distortions, Medium Access Control (MAC) protocols, radio signal types, and cellular systems. Specifically, we utilize deep learning methods in those identification and classification tasks. Performance evaluations demonstrate the effectiveness of deep learning in those radio spectrum awareness tasks.
Tsinghua University Press
Yu Zhou, Hatim Alhazmi, Mohsen H. Alhazmi, Alhussain Almarhabi, Mofadal Alymani, Mingju He, Shengliang Peng, Abdullah Samarkandi, Zikang Sheng, Huaxia Wang, Yu-Dong Yao. Radio spectrum awareness using deep learning: Identification of fading channels, signal distortions, medium access control protocols, and cellular systems. Intelligent and Converged Networks 2021, 2(1): 16-29.