Adaptive Cooperative Spectrum Sensing with Double Thresholds for Noise Resilient Cognitive Radio Networks

The precision of identifying primary user (PU) activities has a major impact on spectrum sensing performance in cognitive radio networks (CRNs). Conventional energy detection techniques frequently come across difficulties including threshold mismatch brought on by noisy power uncertainty, which lowers detection efficiency. The solution to this problem is a double threshold cooperative spectrum sensing (CSS) method. This technique uses secondary users (SUs) collaboration to improve detection accuracy while dynamically modifying thresholds in response to current noise conditions. The dual-threshold mechanism successfully reduces the influence of noise uncertainty by minimizing missed detections and false alarms. According to simulation studies, the suggested approach performs better in terms of detection probability and robustness in a variety of noise situations than conventional single threshold schemes and alternative cooperative approaches. Also, the proposed method compared with cyclostationary feature detection for single and double threshold. The suggested double threshold ED method achieves 98% of detection probability when compared to cyclostationary detection method. This method offers a dependable way to increase CRN spectrum use.