Deep Learning Frameworks for Cognitive Radio Networks: Review and Open Research Challenges

TL;DR

This paper reviews how deep learning enhances cognitive radio networks by addressing key challenges like spectrum sensing and security, highlighting open research issues for future B5G/6G wireless systems.

Contribution

It provides a comprehensive review of deep learning applications in cognitive radio networks and discusses open research challenges for future wireless technologies.

Findings

Deep learning improves spectrum sensing accuracy.

Enhances security and resource management.

Identifies key open research challenges.

Abstract

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.