Learning-Based Cooperative Spectrum Sensing in Hybrid Underlay-Interweave Secondary Networks

In this paper, a cooperative Secondary Network (SN) is proposed that operates under a hybrid underlay-interweave model. The narrowband sensing problem under the interweave model was formulated as a binary hypothesis problem. The Fusion Center (FC) uses learning techniques, namely the Gaussian Mixture Model (GMM), Support Vector Machine (SVM), and Naive Bayes' (NB) to classify the state of the channel. An SVM kernel was chosen to best fit our hybrid network, since a nonlinear relationship exists between the energy vectors collected at the FC. Furthermore, the degree of the polynomial SVM kernel was manipulated to minimize classification errors. The multi-class SVM (MSVM) algorithm was reformulated to fit our multiple hypothesis problem in the underlay model. The performance of the hybrid network was evaluated based on the Receiver Operating Characteristics (ROC) and classification accuracy. In addition, the accuracy of the MSVM is improved through the cooperation of the SUs. Our results show that the proposed learning-based hybrid model is robust to low SNR environments, and yields an improved performance compared with traditional cooperative sensing techniques. Moreover, we show that the Gaussian SVM kernel surpasses other proposed learning algorithms achieving as high as an 80% detection rate with as low as 10% false alarm.