Radio spectrum awareness using deep learning: Identification of fading channels, signal distortions, medium access control protocols, and cellular systems

作     者：Yu Zhou Hatim Alhazmi Mohsen H.Alhazmi Alhussain Almarhabi Mofadal Alymani Mingju He Shengliang Peng Abdullah Samarkandi Zikang Sheng Huaxia Wang Yu-Dong Yao 

作者机构：Department of Electrical and Computer EngineeringStevens Institute of TechnologyHobokenNJ 07030USA the College of Information Science and EngineeringHuaqiao UniversityXiamen 361021China the College of EngineeringArchitecture and TechnologyOklahoma State UniversityStillwaterOK 74078-1010USA 

出 版 物：《Intelligent and Converged Networks》 (智能与融合网络（英文）)

年 卷 期：2021年第2卷第1期

页      面：16-29页

核心收录：

学科分类：0809[工学-电子科学与技术（可授工学、理学学位）] 08[工学] 

主　　题：cellular system deep learning signal classification spectrum awareness Convolutional Neural Network(CNN) 

摘      要：Radio spectrum awareness,including understanding radio signal activities,is crucial for improving spectrum utilization,detecting security vulnerabilities,and supporting adaptive *** tasks include spectrum sensing,identifying systems and terminals,and understanding various protocol *** 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 ***,we utilize deep learning methods in those identification and classification *** evaluations demonstrate the effectiveness of deep learning in those radio spectrum awareness tasks.