Big Data Processing Architecture for Radio Signals Empowered by Deep Learning: Concept, Experiment, Applications and Challenges
In modern society, the demand for radio spectrum resources is increasing. As the information carriers of wireless transmission data, radio signals exhibit the characteristics of big data in terms of volume, variety, value, and velocity. How to uniformly handle these radio signals and obtain value fr...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2018-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8476607/ |
| _version_ | 1818618241462829056 |
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| author | Shilian Zheng Shichuan Chen Lifeng Yang Jiawei Zhu Zhenxing Luo Junjie Hu Xiaoniu Yang |
| author_facet | Shilian Zheng Shichuan Chen Lifeng Yang Jiawei Zhu Zhenxing Luo Junjie Hu Xiaoniu Yang |
| author_sort | Shilian Zheng |
| collection | DOAJ |
| description | In modern society, the demand for radio spectrum resources is increasing. As the information carriers of wireless transmission data, radio signals exhibit the characteristics of big data in terms of volume, variety, value, and velocity. How to uniformly handle these radio signals and obtain value from them is a problem that needs to be studied. In this paper, a big data processing architecture for radio signals is presented and a new approach of end-to-end signal processing based on deep learning is discussed in detail. The radio signal intelligent search engine is used as an example to verify the architecture, and the system components and experimental results are introduced. In addition, the applications of the architecture in cognitive radio, spectrum monitoring, and cyberspace security are introduced. Finally, challenges are discussed, such as unified representation of radio signal features, distortionless compression of wideband sampled data, and deep neural networks for radio signals. |
| first_indexed | 2024-12-16T17:18:28Z |
| format | Article |
| id | doaj.art-970dafc1591a40758e246c9f5a38de8d |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-16T17:18:28Z |
| publishDate | 2018-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-970dafc1591a40758e246c9f5a38de8d2022-12-21T22:23:14ZengIEEEIEEE Access2169-35362018-01-016559075592210.1109/ACCESS.2018.28727698476607Big Data Processing Architecture for Radio Signals Empowered by Deep Learning: Concept, Experiment, Applications and ChallengesShilian Zheng0Shichuan Chen1Lifeng Yang2Jiawei Zhu3Zhenxing Luo4Junjie Hu5Xiaoniu Yang6https://orcid.org/0000-0003-3117-2211Science and Technology on Communication Information Security Control Laboratory, Jiaxing, ChinaScience and Technology on Communication Information Security Control Laboratory, Jiaxing, ChinaScience and Technology on Communication Information Security Control Laboratory, Jiaxing, ChinaScience and Technology on Communication Information Security Control Laboratory, Jiaxing, ChinaScience and Technology on Communication Information Security Control Laboratory, Jiaxing, ChinaScience and Technology on Communication Information Security Control Laboratory, Jiaxing, ChinaScience and Technology on Communication Information Security Control Laboratory, Jiaxing, ChinaIn modern society, the demand for radio spectrum resources is increasing. As the information carriers of wireless transmission data, radio signals exhibit the characteristics of big data in terms of volume, variety, value, and velocity. How to uniformly handle these radio signals and obtain value from them is a problem that needs to be studied. In this paper, a big data processing architecture for radio signals is presented and a new approach of end-to-end signal processing based on deep learning is discussed in detail. The radio signal intelligent search engine is used as an example to verify the architecture, and the system components and experimental results are introduced. In addition, the applications of the architecture in cognitive radio, spectrum monitoring, and cyberspace security are introduced. Finally, challenges are discussed, such as unified representation of radio signal features, distortionless compression of wideband sampled data, and deep neural networks for radio signals.https://ieeexplore.ieee.org/document/8476607/Radio signalsbig datadeep learningneural networkssearch enginecognitive radio |
| spellingShingle | Shilian Zheng Shichuan Chen Lifeng Yang Jiawei Zhu Zhenxing Luo Junjie Hu Xiaoniu Yang Big Data Processing Architecture for Radio Signals Empowered by Deep Learning: Concept, Experiment, Applications and Challenges IEEE Access Radio signals big data deep learning neural networks search engine cognitive radio |
| title | Big Data Processing Architecture for Radio Signals Empowered by Deep Learning: Concept, Experiment, Applications and Challenges |
| title_full | Big Data Processing Architecture for Radio Signals Empowered by Deep Learning: Concept, Experiment, Applications and Challenges |
| title_fullStr | Big Data Processing Architecture for Radio Signals Empowered by Deep Learning: Concept, Experiment, Applications and Challenges |
| title_full_unstemmed | Big Data Processing Architecture for Radio Signals Empowered by Deep Learning: Concept, Experiment, Applications and Challenges |
| title_short | Big Data Processing Architecture for Radio Signals Empowered by Deep Learning: Concept, Experiment, Applications and Challenges |
| title_sort | big data processing architecture for radio signals empowered by deep learning concept experiment applications and challenges |
| topic | Radio signals big data deep learning neural networks search engine cognitive radio |
| url | https://ieeexplore.ieee.org/document/8476607/ |
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