Self-managed 5G networks

Authors
  • J. Martín-Pérez
  • L. Magoula
  • K. Antevski
  • C. Guimarães
  • J. Baranda
  • C.F. Chiasserini
  • A. Sgambelluri
  • C. Papagianni ORCID logo
  • A. García-Saavedra
  • R. Martínez
  • F. Paolucci
  • S. Barmpounakis
  • L. Valcarenghi
  • C.E. Casetti
  • X. Li
  • C.J. Bernardos
  • D. De Vleeschauwer
  • K. De Schepper
  • P. Kontopoulos
  • N. Koursioumpas
  • C. Puligheddu
  • J. Mangues-Bafalluy
  • E. Zeydan
Publication date 2021
Host editors
  • N. Zincir-Heywood
  • M. Mellia
  • Y. Diao
Book title Communication Networks and Service Management in the Era of Artificial Intelligence and Machine Learning
ISBN
  • 9781119675501
ISBN (electronic)
  • 9781119675525
  • 9781119675440
  • 9781119675518
Series IEEE Press series on Networks and Service Management
Pages (from-to) 69-100
Publisher Hoboken, NJ: Wiley-IEEE Press
Organisations
  • Faculty of Science (FNWI) - Informatics Institute (IVI)
Abstract

Meeting 5G high bandwidth rates, ultra-low latencies, and high reliabilities requires of network infrastructures that automatically increase/decrease the resources based on their customers' demand. An autonomous and dynamic management of a 5G network infrastructure represents a challenge, as any solution must account for the radio access network, data plane traffic, wavelength allocation, network slicing, and network functions' orchestration. Furthermore, federation among administrative domains (ADs) must be considered in the network management. Given the increased dynamicity of 5G networks, artificial intelligence/machine learning (AI/ML) solutions are strong candidates able to learn, and take quick provisioning decisions upon fast changes in network conditions. Therefore, this chapter presents an analysis of the state-of-the-art solutions for 5G networks' management, where AI/ML solutions are discussed and compared with traditional methods. Additionally, the chapter provides a technology overview of both standards, and existing solutions regarding the 5G network management, and directions toward the integration of AI/ML in 5G networks.

Document type Chapter
Language English
Published at https://doi.org/10.1002/9781119675525.ch4
Published at https://ieeexplore.ieee.org/document/9536281
Other links https://www.scopus.com/pages/publications/85147788336
Permalink to this page
Back