Congestion Control Prediction Model for 5G Environment Based on Supervised and Unsupervised Machine Learning Approach

Abstract

With the emergence of 5G technology, congestion control has become a vital challenge to be addressed in order to have efficient communication. There are several congestion control models that have been proposed to control and predict the possible congestion in 5G technology. However, finding the optimal congestion control model is an important yet challenging task. In this paper, we examine the supervised and unsupervised machine learning approaches to the task of predicting the possible node that causes congestion in the 5G environment. Due to the huge variance in the domains of the data set columns, measuring the prediction’s consistency was not an easy task. During our study, we tested twenty-six supervised and seven clustering algorithms. Finally, and based on the performance criteria, we have identified the best five algorithms out of the studied algorithms.

Keywords

• Machine learning
• congestion control
• 5G
• supervised ML
• unsupervised ML