AI-driven Zero Touch Network and Service Management in 5G and Beyond: Challenges and Research Directions

Chafika Benzaid, Tarik Taleb

Research output: Contribution to journalArticleScientificpeer-review

54 Citations (Scopus)
178 Downloads (Pure)

Abstract

The foreseen complexity in operating and managing 5G and beyond networks has propelled the trend toward closed-loop automation of network and service management operations. To this end, the ETSI Zero-touch network and Service Management (ZSM) framework is envisaged as a next-generation management system that aims to have all operational processes and tasks executed automatically, ideally with 100 percent automation. Artificial Intelligence (AI) is envisioned as a key enabler of self-managing capabilities, resulting in lower operational costs, accelerated time-tovalue and reduced risk of human error. Nevertheless, the growing enthusiasm for leveraging AI in a ZSM system should not overlook the potential limitations and risks of using AI techniques. The current paper aims to introduce the ZSM concept and point out the AI-based limitations and risks that need to be addressed in order to make ZSM a reality.

Original languageEnglish
Pages (from-to)186 - 194
Number of pages9
JournalIEEE NETWORK
Volume34
Issue number2
DOIs
Publication statusPublished - Feb 2020
MoE publication typeA1 Journal article-refereed

Keywords

  • 5G
  • ZSM
  • Artificial intelligence
  • Machine learning
  • Network management

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