Abstract
Cognitive radio (CR) technology improves frequency resource usage through unlicensed users’ opportunistic use of unused spectrum bands without disrupting licensed ones. With growth in wireless communication needs, dynamic spectrum access (DSA) has emerged as a fundamental concept in enhancing spectral efficiency. New CR systems are projected to outgrow traditional artificial intelligence (AI) models, adopting reconfigurable network infrastructures with the ability to manage autonomously elements to provide uninterrupted service quality. To aid this development, a metacognitive level providing self-monitoring learning and adaptation is necessary to fine-tune AI-based decisionmaking in real time. A new threshold optimization approach for cognitive radio networks, highlighting detection based on the Maximum-Minimum Eigenvalue (MME) criterion, is the theme of this work. The method combines Markov Decision Processes (MDPs) and Q-Learning to support smart spectrum allocation and adaptive spectrum sensing. By adaptively varying parameters based on feedback from the environment, the method enhances decision-making in uncertain and varying network conditions. Simulation outputs show that the model provides enhanced spectrum efficiency, shorter convergence time, and less interference, while maintaining Quality of Service (QoS) for secondary users. This research advances CR systems by marrying signal detection precision with smart learning paradigms to create the potential for strong, autonomous communication networks that can adapt to dynamic spectral conditions.
Keywords: Cognitive Engine, Cognitive Radio, Markov Decision Process (MDP), Maximum-Minimum Eigenvalue (MME), Q-Learning