Deep Learning Frameworks for Cognitive Radio Networks: Review and Open Research Challenges
It addresses challenges in cognitive radio networks for improving wireless communication, but it is incremental as it reviews existing approaches.
This review article examines how deep learning can tackle key issues in cognitive radio networks, such as spectrum sensing and resource allocation, to enhance efficiency and reliability for future B5G/6G wireless networks.
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.