Energy-efficient channel selection algorithm with reduced collision probability in cognitive radio networks

The growing demand for radio spectrum, driven by widespread communication devices, has led to spectrum congestion. Cognitive radio networks (CRNs) offer a solution by allowing secondary users (SUs) to access primary users' (PUs) channels without causing interference. This paper presents an energy-efficient channel selection algorithm aimed at reducing collision probability and energy consumption during spectrum sensing. A prediction-based model forecasts PU channel availability, enabling selective sensing. The results show a significant reduction in energy consumption, with the proposed method consuming 57.9% energy compared to 63.7% for a recent existing method. The proposed approach enhances spectrum management in next-generation wireless systems by minimizing interference and optimizing energy use.