深度学习助力认知无线电网络应对频谱感知等核心挑战
Deep Learning Frameworks for Cognitive Radio Networks: Review and Open Research Challenges
- 综述深度学习在频谱感知、资源分配等场景的应用思路
- 指出其可显著提升网络自适应能力与系统可靠性
- 适合关注B5G/6G无线网络技术演进的研究者
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、资源分配及安全攻击等关键问题的有力工具。将深度学习技术应用于认知无线电网络，可显著增强网络对动态环境的适应能力，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的需求持续增长，未来B5G/6G无线网络有望支持大量新型服务与应用。因此，深度学习在应对认知无线电网络挑战中的重要性不容忽视。本文综述了潜在解决方案，为未来B5G/6G服务的发展提供基础参考。通过深度学习赋能，认知无线电网络将推动下一代无线网络实现更高数据速率、更强可靠性和更好安全性。
原文摘要 · Abstract (English)
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.
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