A Proposed Scheme for Dynamic Threshold Versus Noise Uncertainty in Cognitive Radio Networks (DTNU)

Cognitive radio essentially depends on optimum spectrum sensing for primary user detection. Noise uncertainty in spectrum sensing makes the detection process with fixed threshold unreliable due to thermal noise and interference from other remote communication systems, which in turn results in variation in the signal to noise ratio (SNR). In this paper, a dynamic detection threshold under noise uncertainty scheme is proposed for spectrum sensing to improve the detection performance in an environment characterized with noise uncertainty and low SNR. Hence, the detection threshold at each secondary user is dynamically changing according to the predefined detection and false alarm probabilities together with the received SNR at each node. Furthermore, our proposed integrated algorithm aims at finding the targeted number of samples, sensing time and user’s throughput, while maintaining the detection performance metrics within the desired thresholds. A derived mathematical model and computer simulations are provided to show the influence of the dynamic threshold on system performance, and proof the robustness of our proposed scheme under noise uncertainty environment. Our results show a considerable reduction in number of sensed samples (up to 27%) compared to the approach in literature under low SNR.