Deep Learning Frameworks for Cognitive Radio Networks: Review and Open Research Challenges
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866909373780983808 |
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| author | Jagatheesaperumal, Senthil Kumar Ahmad, Ijaz Höyhtyä, Marko Khan, Suleman Gurtov, Andrei |
| author_facet | Jagatheesaperumal, Senthil Kumar Ahmad, Ijaz Höyhtyä, Marko Khan, Suleman Gurtov, Andrei |
| contents | Deep learning has been proven to be a powerful tool for addressing the most significant issues in cognitive radio networks, such as spectrum sensing, spectrum sharing, resource allocation, and security attacks. The utilization of deep learning techniques in cognitive radio networks can significantly enhance the network's capability to adapt to changing environments and improve the overall system's efficiency and reliability. As the demand for higher data rates and connectivity increases, B5G/6G wireless networks are expected to enable new services and applications significantly. Therefore, the significance of deep learning in addressing cognitive radio network challenges cannot be overstated. This review article provides valuable insights into potential solutions that can serve as a foundation for the development of future B5G/6G services. By leveraging the power of deep learning, cognitive radio networks can pave the way for the next generation of wireless networks capable of meeting the ever-increasing demands for higher data rates, improved reliability, and security. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2410_23949 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Deep Learning Frameworks for Cognitive Radio Networks: Review and Open Research Challenges Jagatheesaperumal, Senthil Kumar Ahmad, Ijaz Höyhtyä, Marko Khan, Suleman Gurtov, Andrei Networking and Internet Architecture Machine Learning 68T07, 90B18 I.2.8; H.3.3; D.2.8; D.3.5; H.3.5 Deep learning has been proven to be a powerful tool for addressing the most significant issues in cognitive radio networks, such as spectrum sensing, spectrum sharing, resource allocation, and security attacks. The utilization of deep learning techniques in cognitive radio networks can significantly enhance the network's capability to adapt to changing environments and improve the overall system's efficiency and reliability. As the demand for higher data rates and connectivity increases, B5G/6G wireless networks are expected to enable new services and applications significantly. Therefore, the significance of deep learning in addressing cognitive radio network challenges cannot be overstated. This review article provides valuable insights into potential solutions that can serve as a foundation for the development of future B5G/6G services. By leveraging the power of deep learning, cognitive radio networks can pave the way for the next generation of wireless networks capable of meeting the ever-increasing demands for higher data rates, improved reliability, and security. |
| title | Deep Learning Frameworks for Cognitive Radio Networks: Review and Open Research Challenges |
| topic | Networking and Internet Architecture Machine Learning 68T07, 90B18 I.2.8; H.3.3; D.2.8; D.3.5; H.3.5 |
| url | https://arxiv.org/abs/2410.23949 |