Survey of AI and ML Techniques for Enhancing Performance of 5G Cognitive Radio Networks

The importance of Cognitive Radio (CR) and Artificial Intelligence (AI) in 5G communication systems is paramount, as these technologies provide effective solutions to critical issues such as spectrum scarcity, escalating data demands, and network congestion. CR facilitates dynamic spectrum access, enabling Secondary Users (SUs) to exploit underutilized licensed spectrum without causing interference to Primary Users (PUs). This paper investigates the integration of AI and machine learning (ML) techniques into Cognitive Radio Networks, focusing on main functions: spectrum sensing, spectrum management, spectrum sharing, and spectrum mobility. Various spectrum handoff strategies including proactive, reactive, and hybrid approaches are discussed to illustrate the balance between latency and operational efficiency. AI-driven techniques such as Q-learning, reinforcement learning, and swarm intelligence are employed to optimize spectrum handoff, enhance decision-making processes, and mitigate service disruptions. Additionally, optimization algorithms like Artificial Bee Colony (ABC), Ant Colony Optimization (ACO), and Particle Swarm Optimization (PSO) are reviewed how to significantly increase spectrum availability while minimizing handoff delays in the domain of CRNs. The potential for incorporating advanced ML models, such as transformers and generative adversarial networks, is also examined to further enhance spectrum efficiency within 5G environments. These innovations are critical for maintaining continuous, high-quality communication in next-generation networks.