Machine Learning-Based Algorithm for Channel Selection Utilizing Preemptive Resume Priority in Cognitive Radio Networks Validated by NS-2

Abstract

This paper utilizes the cognitive radio (CR) spectrum to the fullest extent for extended applications requiring discretion. The CR technology provides various supports for cognitive radio networks (CRNs). The latter has CR nodes that sense free channels. Then, the CRN allocates the unused channels to secondary users (SUs) or unlicensed users. This allocation is termed the spectrum handoff. In this paper, by considering the identical channels in CR networks, a novel machine learning algorithm (the support vector machine—SVM) is employed. In addition, the queuing model of the preemptive resume priority M/M/1 is used. The proposed spectrum handoff algorithm selects the best possible CR network channel. The spectrum handoff algorithm uses the stated SVM algorithm scheme, which covers the transmitted and received power, the minimum service time, the data rate and the maximum vacancy time for the SU, to attain the maximum throughput. However, in multi-user greedy channel selection (GCS), only two parameters are considered. The proposed spectrum handoff algorithm based on the SVM scheme enhances the performance, and the SU throughput is improved to 68.7%. This approach is better than the GCS channel selection scheme. Additionally, this approach decreases the number of spectrum handoffs. As a result, the training accuracy of the SVM method is 97.6%, and it outperforms conventional methods.