Unsupervised Two-Stage Learning Framework for Cooperative Spectrum Sensing

A cognitive radio (CR) network consists of wireless devices that opportunistically borrow vacant licensed bands. Cognitive users adaptively employ a perception-action decision cycle. Learning-based CR networks use past acquired knowledge of the radio environment to make smarter decisions. CR systems that use supervised learning for spectrum sensing require labeled data for training purposes. Having readily available labeled data is a complex task for CR networks, as it requires cooperation between the primary and secondary users. Such cooperation is not possible in interweave CR networks and imposes a cooperation overhead. Motivated by the above, we tackle the problem of labeled data scarcity in practical learning-based CR networks. We propose an unsupervised two-stage learning framework for cooperative spectrum sensing. The system combines the superior performance of the Support Vector Machine (SVM) and low cost training data of the Gaussian Mixture Model (GMM). A system model is proposed, and the system’s performance is evaluated based on the Receiver Operating Characteristics (ROC) and Area Under the ROC Curve (AUC). We obtain an upper and a lower performance bounds in terms of the AUC. The detection performance is compared for the SVM, GMM, and the proposed two-stage system based on the ROC. Additionally, we evaluate the detection performance of the CR network under different primary network sizes. Our results show that the two-stage learning approach attains a higher detection performance than the GMM algorithm, and achieves the same or comparable performance to the SVM algorithm.