Siegel distance-based fusion strategy and differential evolution algorithm for cooperative spectrum sensing

Abstract

Cooperative spectrum sensing (CSS) offers cognitive radio networks a promising solution to efficiently address the issue of spectrum scarcity. To enhance the detection performance of CSS, we investigate a novel cooperative spectrum sensing scheme that incorporates a data fusion strategy based on Siegel distance and a differential evolution algorithm. Specifically, we utilize the Siegel distance-based data fusion strategy to acquire effective signal features. Additionally, to tackle the challenge of deriving the decision threshold, we design a novel symmetrized Kullback-Leibler divergence-based differential evolution algorithm to train the decision classifier, enabling it to detect the availability of licensed spectrum. Finally, we present simulation results that clearly demonstrate the effectiveness of our developed scheme.