Similarity measure-based spectrum sensing algorithm under impulsive noise

Spectrum sensing technology can effectively improves the utilization of spectrum resources by empowering unauthorized users to access the idle spectrum opportunely. This paper proposes a new spectrum sensing algorithm to determine whether a signal exists under impulsive noise by only using the sampled data. First, this method uses the Similarity Measure of samples to construct the test statistic. Theoretical analysis shows that it can suppress the influence of impulsive noise, realize accurate signal detection and improve the reliability of the system with fewer samples. Then, the probability distribution of test statistic is investigated through statistical theory analysis, under the both case of the absence and the presence of primary user signals, respectively, and the probabilities of detection and the false alarm are derived. After that, we give the detection threshold for a given false-alarm probability. Finally, experimental simulation confirms the effectiveness of the proposed algorithm and the correctness of theoretical analysis.