Softwarization, Virtualization, & Machine Learning For Intelligent & Effective V2X Communications

TL;DR

This paper explores how softwarization, virtualization, and machine learning can enhance 5G V2X communications by improving flexibility, scalability, and security through innovative network paradigms.

Contribution

It provides a comprehensive overview of applying softwarization, virtualization, and machine learning to address V2X communication challenges in 5G networks.

Findings

Enhanced flexibility and scalability in V2X networks.

Improved security through software-defined approaches.

Potential for more adaptive and intelligent V2X systems.

Abstract

The concept of the fifth generation (5G) mobile network system has emerged in recent years as telecommunication operators and service providers look to upgrade their infrastructure and delivery modes to meet the growing demand. Concepts such as softwarization, virtualization, and machine learning will be key components as innovative and flexible enablers of such networks. In particular, paradigms such as software-defined networks, software-defined perimeter, cloud & edge computing, and network function virtualization will play a major role in addressing several 5G networks' challenges, especially in terms of flexibility, programmability, scalability, and security. In this work, the role and potential of these paradigms in the context of V2X communication is discussed. To do so, the paper starts off by providing an overview and background of V2X communications. Then, the paper discusses in more details the various challenges facing V2X communications and some of the previous literature work done to tackle them. Furthermore, the paper describes how softwarization, virtualization, and machine learning can be adapted to tackle the challenges of such networks.