Radio spectrum awareness using deep learning: Identification of fading channels, signal distortions, medium access control protocols, and cellular systems
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 pr...
| Main Authors: | , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Tsinghua University Press
2021-03-01
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| Series: | Intelligent and Converged Networks |
| Subjects: | |
| Online Access: | https://www.sciopen.com/article/10.23919/ICN.2021.0004 |
| _version_ | 1797990720225673216 |
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| author | Yu Zhou Hatim Alhazmi Mohsen H. Alhazmi Alhussain Almarhabi Mofadal Alymani Mingju He Shengliang Peng Abdullah Samarkandi Zikang Sheng Huaxia Wang Yu-Dong Yao |
| author_facet | Yu Zhou Hatim Alhazmi Mohsen H. Alhazmi Alhussain Almarhabi Mofadal Alymani Mingju He Shengliang Peng Abdullah Samarkandi Zikang Sheng Huaxia Wang Yu-Dong Yao |
| author_sort | Yu Zhou |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-11T08:41:12Z |
| format | Article |
| id | doaj.art-8edf24ccc126428fbb35ef6ebb72c8d0 |
| institution | Directory Open Access Journal |
| issn | 2708-6240 |
| language | English |
| last_indexed | 2024-04-11T08:41:12Z |
| publishDate | 2021-03-01 |
| publisher | Tsinghua University Press |
| record_format | Article |
| series | Intelligent and Converged Networks |
| spelling | doaj.art-8edf24ccc126428fbb35ef6ebb72c8d02022-12-22T04:34:12ZengTsinghua University PressIntelligent and Converged Networks2708-62402021-03-0121162910.23919/ICN.2021.0004Radio spectrum awareness using deep learning: Identification of fading channels, signal distortions, medium access control protocols, and cellular systemsYu Zhou0Hatim Alhazmi1Mohsen H. Alhazmi2Alhussain Almarhabi3Mofadal Alymani4Mingju He5Shengliang Peng6Abdullah Samarkandi7Zikang Sheng8Huaxia Wang9Yu-Dong Yao10<institution content-type="dept">Department of Electrical and Computer Engineering</institution>, <institution>Stevens Institute of Technology</institution>, <city>Hoboken</city>, <state>NJ</state> <postal-code>07030</postal-code>, <country>USA</country><institution content-type="dept">Department of Electrical and Computer Engineering</institution>, <institution>Stevens Institute of Technology</institution>, <city>Hoboken</city>, <state>NJ</state> <postal-code>07030</postal-code>, <country>USA</country><institution content-type="dept">Department of Electrical and Computer Engineering</institution>, <institution>Stevens Institute of Technology</institution>, <city>Hoboken</city>, <state>NJ</state> <postal-code>07030</postal-code>, <country>USA</country><institution content-type="dept">Department of Electrical and Computer Engineering</institution>, <institution>Stevens Institute of Technology</institution>, <city>Hoboken</city>, <state>NJ</state> <postal-code>07030</postal-code>, <country>USA</country><institution content-type="dept">Department of Electrical and Computer Engineering</institution>, <institution>Stevens Institute of Technology</institution>, <city>Hoboken</city>, <state>NJ</state> <postal-code>07030</postal-code>, <country>USA</country><institution content-type="dept">Department of Electrical and Computer Engineering</institution>, <institution>Stevens Institute of Technology</institution>, <city>Hoboken</city>, <state>NJ</state> <postal-code>07030</postal-code>, <country>USA</country><institution>College of Information Science and Engineering, Huaqiao University</institution>, <city>Xiamen</city> <postal-code>361021</postal-code>, <country>China</country><institution content-type="dept">Department of Electrical and Computer Engineering</institution>, <institution>Stevens Institute of Technology</institution>, <city>Hoboken</city>, <state>NJ</state> <postal-code>07030</postal-code>, <country>USA</country><institution content-type="dept">Department of Electrical and Computer Engineering</institution>, <institution>Stevens Institute of Technology</institution>, <city>Hoboken</city>, <state>NJ</state> <postal-code>07030</postal-code>, <country>USA</country><institution>College of Engineering, Architecture and Technology, Oklahoma State University</institution>, <city>Stillwater</city>, <state>OK</state> <postal-code>74078-1010</postal-code>, <country>USA</country><institution content-type="dept">Department of Electrical and Computer Engineering</institution>, <institution>Stevens Institute of Technology</institution>, <city>Hoboken</city>, <state>NJ</state> <postal-code>07030</postal-code>, <country>USA</country>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.https://www.sciopen.com/article/10.23919/ICN.2021.0004cellular systemdeep learningsignal classificationspectrum awarenessconvolutional neural network (cnn) |
| spellingShingle | 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 cellular system deep learning signal classification spectrum awareness convolutional neural network (cnn) |
| title | Radio spectrum awareness using deep learning: Identification of fading channels, signal distortions, medium access control protocols, and cellular systems |
| title_full | Radio spectrum awareness using deep learning: Identification of fading channels, signal distortions, medium access control protocols, and cellular systems |
| title_fullStr | Radio spectrum awareness using deep learning: Identification of fading channels, signal distortions, medium access control protocols, and cellular systems |
| title_full_unstemmed | Radio spectrum awareness using deep learning: Identification of fading channels, signal distortions, medium access control protocols, and cellular systems |
| title_short | Radio spectrum awareness using deep learning: Identification of fading channels, signal distortions, medium access control protocols, and cellular systems |
| title_sort | radio spectrum awareness using deep learning identification of fading channels signal distortions medium access control protocols and cellular systems |
| topic | cellular system deep learning signal classification spectrum awareness convolutional neural network (cnn) |
| url | https://www.sciopen.com/article/10.23919/ICN.2021.0004 |
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