Recent Advances in Machine Learning for Network Automation in the O-RAN
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Author
Hamdan, Mutasem
Lee, Haeyoung
Triantafyllopoulou, Dionysia
Borralho, Rúben
Kose, Abdulkadir
Amiri, Esmaeil
Mulvey, David
Yu, Wenjuan
Zitouni, Rafik
Pozza, Riccardo
Hunt, Bernie
Bagheri, Hamidreza
Foh, Chuan Heng
Heliot, Fabien
Chen, Gaojie
Xiao, Pei
Wang, Ning
Tafazolli, Rahim
Attention
2299/27102
Abstract
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.