"Adaptive Congestion Control in 5G Networks: Integrating Supervised and Unsupervised Machine Learning Techniques for Real-Time Traffic Management"

Abstract

With the advent of 5G technology, managing network traffic congestion efficiently has become crucial. This paper introduces an advanced congestion control prediction model that employs both supervised and unsupervised machine learning techniques to predict and mitigate congestion in 5G environments. The study evaluates 26 supervised learning algorithms and 7 clustering algorithms, identifying the most effective models based on accuracy, precision, and recall. Integrating these models into 5G networks enhances real-time traffic management, improves user experience, and optimizes network efficiency in complex urban environments.