Research on Spectrum Sensing Algorithms Under Impulsive Noise

To address the problem that the detection performance of existing spectrum sensing algorithms degrades or even fails under impulsive noise, this paper proposes a generalized energy detection-based spectrum sensing algorithm. Theoretical analysis verifies that the proposed algorithm can effectively mitigate the adverse effects of impulsive noise, realize high-precision signal detection, and enhance system reliability with fewer samples. Furthermore, through statistical theoretical analysis, the probability density function of the detection statistic is provided for both scenarios where the primary user signal is absent and present. The probabilities of false alarm and missed detection are also derived, and the threshold corresponding to a prescribed false alarm probability is determined. Finally, simulation results demonstrate the effectiveness of the generalized energy detection algorithm.