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

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

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

Abstract

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.

Original languageEnglish (US)
Pages (from-to)16-29
Number of pages14
JournalIntelligent and Converged Networks
Volume2
Issue number1
DOIs
StatePublished - Mar 1 2021
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications

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