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Recent Advances in Machine Learning for Network Automation in the O-RAN
Authors
Esmaeil Amiri
Hamidreza Bagheri
+16 more
Rúben Borralho
Gaojie Chen
Chuan Heng Foh
Mutasem Hamdan
Fabien Heliot
Bernie Hunt
Abdulkadir Kose
Haeyoung Lee
David Mulvey
Riccardo Pozza
Rahim Tafazolli
Dionysia Triantafyllopoulou
Ning Wang
Pei Xiao
Wenjuan Yu
Rafik Zitouni
Publication date
28 October 2023
Publisher
Doi
Cite
Abstract
© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY), https://creativecommons.org/licenses/by/4.0/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.Peer reviewe
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University of Hertfordshire Research Archive
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Last time updated on 13/11/2023
York St John University Institutional Repository
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Last time updated on 20/11/2023