Paper Info
Title
Cooperative spectrum sensing in cognitive radio networks using multi-class support vector machine algorithms
Abstract
This paper addresses the problem of spectrum sensing in cognitive radio networks under multiple primary users condition using multi-class support vector machine (SVM) algorithms. First, we formulated the spectrum sensing problem under multiple primary users scenario as a multiple class signal detection problem where each class is comprised of one or more sub-classes and generalized expressions for the possible classes are provided. Next, we investigate the performance of energy based features and the error correcting output codes (ECOC) based multi-class SVM algorithms for solving the multi-class spectrum sensing problem using two different coding strategies. The performance of the proposed detector is quantified in terms of receiver operating characteristics curves and classification accuracy. Simulation results show that the proposed detector is robust to joint spatio-temporal detection of spectrum holes in cognitive radio networks.
Year
DOI
Venue
2015
10.1109/ICSPCS.2015.7391780
2015 9th International Conference on Signal Processing and Communication Systems (ICSPCS)
Keywords
Field
DocType
Cognitive radio,spectrum sensing,machine learning,multi-class support vector machine,multiple primary users
Receiver operating characteristic,Expression (mathematics),Pattern recognition,Detection theory,Computer science,Support vector machine,Algorithm,Coding (social sciences),Artificial intelligence,Detector,Signal processing algorithms,Cognitive radio
Conference
Citations 
PageRank 
References 
0
0.34
13
Authors
2
Name
Order
Citations
PageRank
Olusegun Peter Awe161.83
Sangarapillai Lambotharan268769.79