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Open Access

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

Department of Electrical and Computer Engineering, Stevens Institute of Technology, Hoboken, NJ 07030, USA
College of Information Science and Engineering, Huaqiao University, Xiamen 361021, China
College of Engineering, Architecture and Technology, Oklahoma State University, Stillwater, OK 74078-1010, USA
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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.

Keywords

cellular systemdeep learningsignal classificationspectrum awarenessConvolutional Neural Network (CNN)

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Intelligent and Converged Networks
Volume 2 Issue 1,
March 2021
Pages 16-29
DOI: 10.23919/ICN.2021.0004
Cite this article:
Zhou Y, Alhazmi H, Alhazmi MH, et al. 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. https://doi.org/10.23919/ICN.2021.0004

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Received: 17 January 2021
Accepted: 26 January 2021
Published: 12 May 2021
© ITU and TUP 2021

This work is available under the CC BY-NC-ND 3.0 IGO license: https://creativecommons.org/licenses/by-nc-nd/3.0/igo/.
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