Advancements in Machine Learning Techniques for Optimizing Cognitive Radio Networks: A Comprehensive Review

Abstract

Machine learning (ML) techniques have gained significant attention in the field of cognitive radio networks (CRNs) due to their ability to learn and adapt to changing environments. In CRNs, ML algorithms can be used for various tasks such as spectrum sensing, spectrum allocation, power control, and cognitive routing. This literature survey provides an overview of the state-of-the-art machine learning approaches for CRNs, including reinforcement learning, deep learning, decision trees, and genetic algorithms. The potential applications of these approaches, as well as the challenges and opportunities for future research, are also discussed. The survey can serve as a valuable resource for researchers and practitioners interested in applying machine learning in CRNs.