Machine Learning Techniques for Cooperative Spectrum Sensing in Cognitive Radio Networks

• Published in: IEEE journal on selected areas in communications Vol. 31; no. 11; pp. 2209 - 2221
• Main Authors: Thilina, Karaputugala Madushan, Kae Won Choi, Saquib, Nazmus, Hossain, Ekram
• Format: Journal Article
• Language: English
• Published: IEEE 01-11-2013
• Subjects: Availability; Cascading style sheets; Cognitive radio; cooperative spectrum sensing; Energy states; GMM; K-means clustering; K-nearest-neighbor; primary user detection; Sensors; support vector machine (SVM); Support vector machines; Training; Vectors
• ISSN: 0733-8716; 1558-0008
• DOI: 10.1109/JSAC.2013.131120

We propose novel cooperative spectrum sensing (CSS) algorithms for cognitive radio (CR) networks based on machine learning techniques which are used for pattern classification. In this regard, unsupervised (e.g., K-means clustering and Gaussian mixture model (GMM)) and supervised (e.g., support vector machine (SVM) and weighted K-nearest-neighbor (KNN)) learning-based classification techniques are implemented for CSS. For a radio channel, the vector of the energy levels estimated at CR devices is treated as a feature vector and fed into a classifier to decide whether the channel is available or not. The classifier categorizes each feature vector into either of the two classes, namely, the "channel available class" and the "channel unavailable class". Prior to the online classification, the classifier needs to go through a training phase. For classification, the K-means clustering algorithm partitions the training feature vectors into K clusters, where each cluster corresponds to a combined state of primary users (PUs) and then the classifier determines the class the test energy vector belongs to. The GMM obtains a mixture of Gaussian density functions that well describes the training feature vectors. In the case of the SVM, the support vectors (i.e., a subset of training vectors which fully specify the decision function) are obtained by maximizing the margin between the separating hyperplane and the training feature vectors. Furthermore, the weighted KNN classification technique is proposed for CSS for which the weight of each feature vector is calculated by evaluating the area under the receiver operating characteristic (ROC) curve of that feature vector. The performance of each classification technique is quantified in terms of the average training time, the sample classification delay, and the ROC curve. Our comparative results clearly reveal that the proposed algorithms outperform the existing state-of-the-art CSS techniques.