Machine Learning to Data Fusion Approach for Cooperative Spectrum Sensing

Cooperative spectrum sensing has been shown to be an effective method to improve the detection performance of the licensed user availability by exploiting spatial diversity. However, cooperation among cognitive radio (CR) users may also introduce a variety of overheads due to the extra sensing time, delay, energy, and operations that limit achievable cooperative gain. In responding to this paper, we propose a machine learning based fusion center algorithm that can provide real time per frame training and decision based cooperative spectrum sensing. The new fusion algorithm based on training a machine learning classifier over a set containing some frame energy test statistics along with their corresponding decisions about the presence or absence of the primary user (PU) transmission, so as to predict the decisions for new frames with new energy test statistics. The simulation and numerical results show that the new approach performs the same as the current fusion rule with less sensing time, delay and operations. In this paper we also present a simulation comparison of four supervised machine learning classifiers: K-nearest neighbor (KNN), support vector machine (SVM), Naive Bayes (NB), and Decision Tree (DT) in classifying 1000 testing frames after training these classifiers over a set containing 1000 frames. It shows that KNN and DT classifier outperform the other two classifiers in the accuracy of classifying the new frames.