arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2006.04595cs.NIcs.LG
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软件化、虚拟化和机器学习实现智能高效的V2X通信

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

Electrical & Computer Engineering Department, Western University(西安大略大学电气与计算机工程系)

Abdallah Moubayed, Abdallah Shami

摘要:

近年来,随着电信运营商和服务提供商寻求升级其基础设施和交付模式以满足不断增长的需求,第五代(5G)移动网络系统的概念已经出现。软件化、虚拟化和机器学习等概念将成为此类网络的创新和灵活推动者。特别是,软件定义网络、软件定义边界、云计算和边缘计算以及网络功能虚拟化等范式将在解决5G网络的挑战方面发挥重要作用,特别是在灵活性、可编程性、可扩展性和安全性方面。在这项工作中,这些范例在V2X通信的背景下的作用和潜力进行了讨论。为此,本文首先提供V2X通信的概述和背景。然后,本文更详细地讨论了V2X通信面临的各种挑战,以及之前为解决这些挑战所做的一些文献工作。此外,本文还介绍了如何采用软件化、虚拟化和机器学习来应对此类网络的挑战。

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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