Experimental Evaluation of Spectrum Handoff Management with Machine Learning Algorithms Using Software Defined Radio

Although the design of spectrum switching has been studied, little is known about how random user movement affects the handoff. This issue can occur when a user moves to a new location. In this paper, the authors present a framework that verifies the necessity of spectrum handoff to improve the performance of the system by employing machine learning (ML) techniques. Some of these include the Logistic Regression, KNN Algorithm, SVM Algorithm, Naïve Bayes Classifier, Decision Tree Classification and Random Forest Algorithm. The system is implemented on a real-time dataset where all the users are separated in power domain using the concept of non-orthogonal multiple access (NOMA) technique. The dataset values are prepared using a software-defined radio experimental setup, which is used to analyse the performance of various ML techniques in terms of confusion matrix, specificity, precision, F1_score, sensitivity and accuracy. The performance of proposed system is compared with the literature and shown a significant improvement that proves the evidence of our findings.