A Fuzzy Support Vector Machine Algorithm for Cooperative Spectrum Sensing with Noise Uncertainty

In cognitive radio networks, the performance of energy detection will be degraded significantly due to the cluster overlapping caused by noise uncertainty. To alleviate the noise uncertainty effect, a novel machine learning algorithm is proposed in this paper for cooperative spectrum sensing. The proposed algorithm incorporates fuzzy support vector machine and nonparallel hyperplane support vector machine. For membership assignment, kernel shadow c-means (KSCM) algorithm is utilized. Furthermore, the test statistics collected by the second users are arranged into a feature vector instead of being combined through weighted sum. Simulations results have shown that the proposed scheme, called NP-FSVM, is more robust to noise uncertainty than the existing methods.

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