CommRad RF: A dataset of communication radio signals for detection, identification and classificationZenodo
The rapid growth in wireless technology has revolutionized the way of living but at the same time, raising security concerns of unauthorized access of spectrum, both military and commercial sectors. The subject of Radio Frequency (RF) fingerprinting has got special attention in recent years. Researc...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-04-01
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| Series: | Data in Brief |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352340925001192 |
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| author | Muhammad Usama Zahid Muhammad Usman Akram Muhammad Danish Nisar Fahd Maqsood Syed Usman Ali Muhammad Montaha |
| author_facet | Muhammad Usama Zahid Muhammad Usman Akram Muhammad Danish Nisar Fahd Maqsood Syed Usman Ali Muhammad Montaha |
| author_sort | Muhammad Usama Zahid |
| collection | DOAJ |
| description | The rapid growth in wireless technology has revolutionized the way of living but at the same time, raising security concerns of unauthorized access of spectrum, both military and commercial sectors. The subject of Radio Frequency (RF) fingerprinting has got special attention in recent years. Researchers proposed various datasets of radio signals of different types of devices (drones, cell phones, IoT, and Radar). However, presently there is no freely available dataset on walkie-talkies/commercial radios. To fill out the void, we present an innovative dataset including more than 2700 radio signals captured from 27 radios located in an indoor multipath environment. This dataset can enhance the security of the communication channels by providing the possibility to analyse and detect any unauthorized source of transmission. Furthermore, we also propose two innovative deep learning models named Light Weight 1DCNN and Light Weight Bivariate 1DCNN, for efficient data processing and learning patterns from the complex dataset of radio signals. |
| format | Article |
| id | doaj-art-5aa070e53e464fed88260a82e4901d44 |
| institution | DOAJ |
| issn | 2352-3409 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Data in Brief |
| spelling | doaj-art-5aa070e53e464fed88260a82e4901d442025-08-20T02:45:50ZengElsevierData in Brief2352-34092025-04-015911138710.1016/j.dib.2025.111387CommRad RF: A dataset of communication radio signals for detection, identification and classificationZenodoMuhammad Usama Zahid0Muhammad Usman Akram1Muhammad Danish Nisar2Fahd Maqsood3Syed Usman Ali4Muhammad Montaha5Department of Electrical Engineering, Sir Syed CASE Institute of Technology, Islamabad 46000, Federal, Pakistan; Corresponding authors.Department of Computer Engineering, National University of Sciences & Technology, Islamabad 44000, Federal, Pakistan; Corresponding authors.Department of Electrical Engineering, Sir Syed CASE Institute of Technology, Islamabad 46000, Federal, PakistanDepartment of Electrical Engineering, National University of Sciences & Technology, Islamabad 44000, Federal, PakistanDepartment of Electrical Engineering, National University of Sciences & Technology, Islamabad 44000, Federal, PakistanDepartment of Computer Engineering, National University of Sciences & Technology, Islamabad 44000, Federal, Pakistan; Corresponding authors.The rapid growth in wireless technology has revolutionized the way of living but at the same time, raising security concerns of unauthorized access of spectrum, both military and commercial sectors. The subject of Radio Frequency (RF) fingerprinting has got special attention in recent years. Researchers proposed various datasets of radio signals of different types of devices (drones, cell phones, IoT, and Radar). However, presently there is no freely available dataset on walkie-talkies/commercial radios. To fill out the void, we present an innovative dataset including more than 2700 radio signals captured from 27 radios located in an indoor multipath environment. This dataset can enhance the security of the communication channels by providing the possibility to analyse and detect any unauthorized source of transmission. Furthermore, we also propose two innovative deep learning models named Light Weight 1DCNN and Light Weight Bivariate 1DCNN, for efficient data processing and learning patterns from the complex dataset of radio signals.http://www.sciencedirect.com/science/article/pii/S2352340925001192Communication radiosRadio frequency fingerprintRadio frequency signal intelligenceRadio classification |
| spellingShingle | Muhammad Usama Zahid Muhammad Usman Akram Muhammad Danish Nisar Fahd Maqsood Syed Usman Ali Muhammad Montaha CommRad RF: A dataset of communication radio signals for detection, identification and classificationZenodo Data in Brief Communication radios Radio frequency fingerprint Radio frequency signal intelligence Radio classification |
| title | CommRad RF: A dataset of communication radio signals for detection, identification and classificationZenodo |
| title_full | CommRad RF: A dataset of communication radio signals for detection, identification and classificationZenodo |
| title_fullStr | CommRad RF: A dataset of communication radio signals for detection, identification and classificationZenodo |
| title_full_unstemmed | CommRad RF: A dataset of communication radio signals for detection, identification and classificationZenodo |
| title_short | CommRad RF: A dataset of communication radio signals for detection, identification and classificationZenodo |
| title_sort | commrad rf a dataset of communication radio signals for detection identification and classificationzenodo |
| topic | Communication radios Radio frequency fingerprint Radio frequency signal intelligence Radio classification |
| url | http://www.sciencedirect.com/science/article/pii/S2352340925001192 |
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