Modeling and Optimizing Cognitive Radio Networks for Efficient Spectrum Allocation

Abstract

Modern wireless communication networks face a fundamental challenge: the scarcity of available frequency spectrum. Studies indicate that a significant proportion of licensed spectrum remains underutilized. Cognitive Radio Networks (CRNs) emerge as a promising solution, enabling secondary users (SUs) to dynamically access unused frequency bands without causing harmful interference to primary users (PUSs). This paper presents a comprehensive mathematical model of spectrum allocation in cognitive radio networks, reviewing various optimization techniques employed in this field, including nature-inspired optimization algorithms, game theory, and deep and reinforcement learning techniques. Detailed MATLAB code for simulating spectrum sensing and dynamic allocation is presented, with analytical results displayed in tables and graphs. The findings suggest that integrating artificial intelligence techniques with optimization algorithms can achieve significant improvements in spectrum efficiency and quality of service.