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Performance Analysis of Support Vector Machine-Based Classifier for Spectrum Sensing in Cognitive Radio Networks

Jan, Sana Ullah; Vu, Van Hiep; Koo, In Soo


Van Hiep Vu

In Soo Koo


In this work, the performance of support vector machine (SVM)-based classifier, applied for spectrum sensing in cognitive radio (CR) networks, is analyzed. A single observation given input to classifier is composed of three statistical features extracted from the primary user (PU) sensing signal and residual energy in percent of the secondary user (SU). The trained classifier predicts PU’s presence based on the input signal. The SU starts transmission if PU is predicted absent, otherwise continues sensing other frequency bands. The hypothesis that PU is absent, is further classified in multi classes. The secondary user varies the transmission power based on the output class. This technique increases the quality of service (QoS) due to low interference from SU to PU even if failed to detect. The cross validation technique increases the generalization of classifier. The performance of classifier is examined in terms of accuracy results. The signal-to-noise (SNR) ratio from PU to SU is varied to investigate effect on classifier’s performance. Furthermore, the receiver operating characteristics (ROC) is presented for more evaluation. The parameter ‘area under curve (AUC)’ is given for comparison. The simulation results show the efficiency of proposed features with SVM-based classifier for spectrum sensing in CR applications.

Presentation Conference Type Conference Paper (Published)
Conference Name 2018 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC)
Start Date Oct 18, 2018
End Date Oct 20, 2018
Online Publication Date Feb 21, 2019
Publication Date 2018
Deposit Date Oct 21, 2022
Publisher Institute of Electrical and Electronics Engineers
Book Title 2018 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC)-
Keywords cognitive-radio, spectrum-sensing, machine-learning, support-vector-machine, feature-extraction
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