Pattern Classification Techniques for Cooperative Spectrum Sensing in Cognitive Radio Networks: SVM and W-KNN Approaches
The authors 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.