Deep Learning Frameworks for Cognitive Radio Networks: Review and Open Research Challenges
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Abstract (arXiv)
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.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 23 of its 217 references that are in the map. First posted on arXiv 31 Oct 2024.
Builds on
23 of 217 references.
References · 23
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11 more
- 2022Machine Learning in NextG Networks via Generative Adversarial Networkscited by 8
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- 2018Deep Reinforcement Learning for Time Scheduling in RF-Powered Backscatter Cognitive Radio Networkscited by 2
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Citing papers per year: 2024: 1; 2025: 1.
2 papers.