Cross layer optimization in AI powered cognitive radio networks for dynamic spectrum access

摘要

The rapid growth of wireless communication, therefore, has led to spectrum scarcity, creating a need for efficient spectrum utilization. Cognitive Radio Networks offer solutions in the form of DSA. However, optimization of spectrum usage remains one of the great challenges because of the dynamic and uncertain nature of the environment in which wireless communications take place. The purpose of this paper is to propose an AI-powered cross-layer optimization framework to enhance DSA in CRNs. The proposed methodology integrates Machine Learning (ML) and Reinforcement Learning (RL) in optimizing spectrum sensing at the physical layer and resource allocation at the MAC layer and interference management at the network layer. The scheme automatically adapts to the changes in spectrum conditions, thus reducing interference and increasing throughput. Simulation results demonstrate that the AI-driven approach enhances spectrum efficiency by 30%, reduces interference by 20%, and increases network throughput by 15% as compared to traditional methods. This work demonstrates that AI-enabled cross-layer optimization in CRNs can significantly improve spectrum utilization, making networks more adaptive and efficient.