Enhancing Energy Efficiency in Cognitive Radio Networks Using Semi‐Markov Models and Energy Harvesting Techniques

ABSTRACT Cognitive radio networks (CRNs) enable efficient spectrum utilization leveraging dynamic spectrum access to optimize bandwidth usage and improve overall network efficiency, but achieving energy efficiency remains a key challenge in CRNs. The proposed study presents a novel framework that integrates energy‐saving and energy‐harvesting strategies in CRNs using a semi‐Markov process to model the dynamic behavior of secondary users (SUs) under an N‐policy. Unlike Markov models, the semi‐Markov models capture non‐exponential sojourn times of energy saving states, providing a more accurate representation of CRN dynamics under varying energy and traffic conditions. The proposed model quantifies key performance metrics, including throughput, latency, and energy consumption. Numerical results demonstrate that our method significantly reduces energy usage while maintaining network performance, offering a sustainable solution for energy‐constrained CRNs. This study highlights the potential of semi‐Markov models to advance green wireless communication systems.