Applications of Machine Learning in Mobile Networking

Abstract

Abstract Communication networks are constantly increasing in size and complexity. Hence, the traditional rule-based algorithms of these networks will probably not operate at their most effective efficiency. Machine learning (ML) is being used these days to solve tough problems in a variety of industries, including banking, healthcare, and enterprise. Communication network performance can be improved using computational models that can deliver ML algorithms. This paper investigates the use of ML models in communication networks for prediction, intruder detection, route and path allocation, quality of service enhancement, and resource management. A review of the current literature suggests that there is a wealth of potential for researchers to leverage ML to solve challenging network performance problems, especially in the development of software-based networks and 5G.