Pattern classification techniques for cooperative spectrum sensing in cognitive radio networks: SVM and W-KNN approaches

We consider novel cooperative spectrum sensing (CSS) algorithms based on the pattern classification techniques for cognitive radio (CR) networks. In this regard, support vector machine (SVM) and weighted K-nearest-neighbor (KNN) classification techniques are implemented for CSS. The received signal strength at the CR users are treated as features and fed into the classifier to detect the availability of the primary user (PU). Each instance of PU activity (i.e., availability and unavailability) is categorized into positive and negative classes (respectively). In the case of SVM, for minimization of classification errors the support vectors are obtained by maximizing the margin between the separating hyperplane and data. Towards this end, we investigate the effect of different kernels through quantifying in terms of detection probability by representing the receiver operating characteristic (ROC) curves. Furthermore, weighted KNN classification technique is proposed for CSS and the corresponding weights are calculated by evaluating the area under ROC curve of each feature. Our comparative results clearly reveal that the proposed SVM and weighted KNN algorithms outperform the existing state-of-the-art pattern classification-based CSS techniques.