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On End-to-End Intelligent Automation of 6G Networks

Author

Listed:
  • Abdallah Moubayed

    (Electrical & Computer Engineering Department, Western University, London, ON N6A 5B9, Canada)

  • Abdallah Shami

    (Electrical & Computer Engineering Department, Western University, London, ON N6A 5B9, Canada)

  • Anwer Al-Dulaimi

    (Mobile Solutions Unit, EXFO Inc., Montreal, QC H4S 0A4, Canada)

Abstract

The digital transformation of businesses and services is currently in full force, opening the world to a new set of unique challenges and opportunities. In this context, 6G promises to be the set of technologies, architectures, and paradigms that will promote the digital transformation and enable growth and sustainability by offering the means to interact and control the digital and virtual worlds that are decoupled from their physical location. One of the main challenges facing 6G networks is “end-to-end network automation”. This is because such networks have to deal with more complex infrastructure and a diverse set of heterogeneous services and fragmented use cases. Accordingly, this paper aims at envisioning the role of different enabling technologies towards end-to-end intelligent automated 6G networks. To this end, this paper first reviews the literature focusing on the orchestration and automation of next-generation networks by discussing in detail the challenges facing efficient and fully automated 6G networks. This includes automating both the operational and functional elements for 6G networks. Additionally, this paper defines some of the key technologies that will play a vital role in addressing the research gaps and tackling the aforementioned challenges. More specifically, it outlines how advanced data-driven paradigms such as reinforcement learning and federated learning can be incorporated into 6G networks for more dynamic, efficient, effective, and intelligent network automation and orchestration.

Suggested Citation

  • Abdallah Moubayed & Abdallah Shami & Anwer Al-Dulaimi, 2022. "On End-to-End Intelligent Automation of 6G Networks," Future Internet, MDPI, vol. 14(6), pages 1-28, May.
    • Handle: RePEc:gam:jftint:v:14:y:2022:i:6:p:165-:d:827027
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    References listed on IDEAS

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    1. Arun Rai, 2020. "Explainable AI: from black box to glass box," Journal of the Academy of Marketing Science, Springer, vol. 48(1), pages 137-141, January.
    2. Zeki Murat Çınar & Abubakar Abdussalam Nuhu & Qasim Zeeshan & Orhan Korhan & Mohammed Asmael & Babak Safaei, 2020. "Machine Learning in Predictive Maintenance towards Sustainable Smart Manufacturing in Industry 4.0," Sustainability, MDPI, vol. 12(19), pages 1-42, October.
    3. Gioele Bigini & Valerio Freschi & Emanuele Lattanzi, 2020. "A Review on Blockchain for the Internet of Medical Things: Definitions, Challenges, Applications, and Vision," Future Internet, MDPI, vol. 12(12), pages 1-16, November.
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