Recent Advances in Machine Learning for Network Automation in the O-RAN

Mutasem Hamdan, Haeyoung Lee, Dionysia Triantafyllopoulou, Rúben Borralho, Abdulkadir Kose, Esmaeil Amiri, David Mulvey, Wenjuan Yu, Rafik Zitouni, Riccardo Pozza, Bernie Hunt, Hamidreza Bagheri, Chuan Heng Foh, Fabien Heliot, Gaojie Chen, Pei Xiao, Ning Wang, Rahim Tafazolli

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The evolution of network technologies has witnessed a paradigm shift toward open and intelligent networks, with the Open Radio Access Network (O-RAN) architecture emerging as a promising solution. O-RAN introduces disaggregation and virtualization, enabling network operators to deploy multi-vendor and interoperable solutions. However, managing and automating the complex O-RAN ecosystem presents numerous challenges. To address this, machine learning (ML) techniques have gained considerable attention in recent years, offering promising avenues for network automation in O-RAN. This paper presents a comprehensive survey of the current research efforts on network automation using ML in O-RAN. We begin by providing an overview of the O-RAN architecture and its key components, highlighting the need for automation. Subsequently, we delve into O-RAN support for ML techniques. The survey then explores challenges in network automation using ML within the O-RAN environment, followed by the existing research studies discussing application of ML algorithms and frameworks for network automation in O-RAN. The survey further discusses the research opportunities by identifying important aspects where ML techniques can benefit.
Original languageEnglish
Article number8792
Pages (from-to)1-35
Number of pages35
Issue number21
Publication statusPublished - 28 Oct 2023


  • open radio access networks; machine learning; artificial intelligence
  • open radio access networks
  • machine learning
  • artificial intelligence


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