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.

翻译：深度学习已被证明是解决认知无线电网络中频谱感知、频谱共享、资源分配和安全攻击等关键问题的有力工具。在认知无线电网络中应用深度学习技术，能够显著增强网络适应动态环境的能力，并提升整体系统的效率与可靠性。随着对更高数据速率和连接性需求的增长，B5G/6G无线网络有望显著推动新型服务与应用的发展。因此，深度学习在应对认知无线电网络挑战方面的重要性不容忽视。本综述文章为潜在解决方案提供了有价值的见解，这些方案可为未来B5G/6G服务的开发奠定基础。通过利用深度学习的能力，认知无线电网络可为下一代无线网络铺平道路，使其能够满足对更高数据速率、更强可靠性与安全性的日益增长的需求。