AI-powered Infrastructures for Intelligence and Automation in Beyond-5G Systems

L. Militano, Anastasios Zafeiropoulos, Eleni Fotopoulou, R. Bruschi, C. Lombardo, Andy Edmonds, S. Papavassiliou
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引用次数: 2

Abstract

In this paper, a vision for beyond-5G systems is proposed where automation and intelligence in cloud-native infrastructures are in focus. Exploiting the convergence of cloud technologies at the edge and mobile communication networks, a set of technological solutions is discussed that will play a fundamental role on the path from 5G towards future 6G systems. Currently, a strong need is felt in the telecommunication world for greater automation to meet the extreme requirements expected for 6G applications. Artificial Intelligence (AI) is gaining momentum as one of the main enabling technologies for beyond-5G networks. Reinforcement Learning (RL) and Federated Learning (FL) are here proposed as technologies to enhance automation and improve the intelligence of orchestration mechanisms of both network services and applications. These technologies are brought together in a comprehensive cloud-native architectural vision to fill the gap between current 5G systems and AI-powered systems of the future.
超5g系统中用于智能和自动化的人工智能基础设施
在本文中,提出了超5g系统的愿景,其中云原生基础设施中的自动化和智能是重点。利用边缘云技术和移动通信网络的融合,讨论了一套技术解决方案,这些解决方案将在从5G到未来6G系统的道路上发挥基础性作用。目前,电信行业迫切需要更高程度的自动化,以满足6G应用的极端要求。人工智能(AI)正在成为超5g网络的主要支持技术之一。强化学习(RL)和联邦学习(FL)在这里被提出作为增强自动化和改进网络服务和应用程序编排机制的智能的技术。这些技术汇集在一个全面的云原生架构愿景中,以填补当前5G系统与未来人工智能系统之间的空白。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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