Collaborative Cross System AI: Toward 5G System and beyond

Miloud Bagaa, Tarik Taleb, Jukka Riekki, Jae Seung Song*

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

8 Citations (Scopus)
108 Downloads (Pure)

Abstract

The emerging industrial verticals set new challenges for 5G and beyond systems. Indeed, the heterogeneity of the underlying technologies and the challenging and conflicting requirements of the verticals make the orchestration and management of networks complicated and challenging. Recent advances in network automation and artificial intelligence (AI) create enthusiasm from industries and academia toward applying these concepts and techniques to tackle these challenges. With these techniques, the network can be autonomously optimized and configured. This article suggests a collaborative cross-system AI that leverages diverse data from different segments involved in the end-to-end communication of a service, diverse AI techniques, and diverse network automation tools to create a self-optimized and self-orchestrated network that can adapt according to the network state. We align the proposed framework with the ongoing network standardization.

Original languageEnglish
Article number9409842
Pages (from-to)286-294
Number of pages9
JournalIEEE NETWORK
Volume35
Issue number4
Early online date20 Apr 2021
DOIs
Publication statusPublished - 1 Jul 2021
MoE publication typeA1 Journal article-refereed

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