Optimal Classifier Based Spectrum Sensing in Cognitive Radio Wireless Systems

In this paper, the authors present and investigate the performance of novel classification schemes for spectrum sensing in cooperative Multiple-Input Multiple-Output (MIMO) wireless Cognitive Radio (CR) networks. In this context, they consider several optimal classification schemes such as Support Vector Classifiers (SVC), Logistic Regression (LR) and Quadratic Discrimination (QD) for primary user detection. It is demonstrated that these classification techniques have a significantly reduced complexity of implementation in practical CR applications compared to conventional likelihood based detection schemes as they do not require knowledge of the channel state information and noise power.