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.

中文翻译：

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认知无线电网络的深度学习框架：回顾和开放研究挑战


深度学习已被证明是解决认知无线电网络中最重要问题的强大工具，例如频谱传感、频谱共享、资源分配和安全攻击。在认知无线电网络中利用深度学习技术可以显著提高网络适应不断变化的环境的能力，并提高整个系统的效率和可靠性。随着对更高数据速率和连接性需求的增加，B5G/6G 无线网络有望显著支持新的服务和应用。因此，深度学习在解决认知无线电网络挑战方面的重要性怎么强调都不为过。这篇评论文章提供了对潜在解决方案的宝贵见解，这些解决方案可以作为未来 B5G/6G 服务开发的基础。通过利用深度学习的力量，认知无线电网络可以为下一代无线网络铺平道路，这些网络能够满足对更高数据速率、更高可靠性和安全性的不断增长的需求。