Assessment of spectrum sensing using support vector machine combined with principal component analysis Online publication date: Tue, 30-Aug-2022
by Manash Mahanta; Attaphongse Taparugssanagorn; Bipun Man Pati
International Journal of Sensor Networks (IJSNET), Vol. 39, No. 4, 2022
Abstract: Cognitive radio (CR) is an up-and-coming technology to rectify the problem of under-utilisation of the allocated spectrum and meet the increasing demand for free spectrum. Spectrum sensing empowers the CR to adjust to its surroundings by locating free spectrum. Although spectrum sensing using a support vector machine (SVM) is already found in literature, an SVM combined with principal component analysis (PCA) and varying the kernel scale is yet to be investigated. In this paper, we perform spectrum sensing using an SVM and evaluate the performances of various kernel functions used in the SVM as well as how the performances of the learning algorithm change as we apply PCA and vary the kernel scales. We then compare the training time of the SVM kernels. Finally, we calculate the contributions of power, variance, skewness, and kurtosis of the received signal towards the decision-making process of the learning algorithm.
Online publication date: Tue, 30-Aug-2022
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Sensor Networks (IJSNET):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email email@example.com