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.

本研究解决了认知无线电网络中存在的多种重大问题，包括频谱感知、频谱共享、资源分配和安全攻击。论文提出通过深度学习技术的应用，可以显著提高网络适应变化环境的能力，提升系统的效率和可靠性。这项工作的最重要发现是，利用深度学习能够为未来的B5G/6G服务的发展提供基础，帮助满足对更高数据速率和安全性的日益增长的需求。

认知无线电网络的深度学习框架：综述与开放研究挑战