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

Hamdan, Mutasem Q. ORCID: https://orcid.org/0000-0003-2331-4021, Lee, Haeyoung ORCID: https://orcid.org/0000-0002-5760-6623, Triantafyllopoulou, Dionysia ORCID: https://orcid.org/0000-0002-8150-4803, Borralho, Rúben, Kose, Abdulkadir ORCID: https://orcid.org/0000-0002-6877-1392, Amiri, Esmaeil ORCID: https://orcid.org/0009-0006-3520-6350, Mulvey, David, Yu, Wenjuan, Zitouni, Rafik, Pozza, Riccardo ORCID: https://orcid.org/0000-0002-8025-9455, Hunt, Bernie, Bagheri, Hamidreza ORCID: https://orcid.org/0000-0002-4372-0281, Foh, Chuan Heng, Heliot, Fabien ORCID: https://orcid.org/0000-0003-3583-3435, Chen, Gaojie ORCID: https://orcid.org/0000-0003-2978-0365, Xiao, Pei, Wang, Ning and Tafazolli, Rahim (2023) Recent Advances in Machine Learning for Network Automation in the O-RAN. Sensors, 23 (21).

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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 usingML 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 forML techniques. The survey then explores challenges in network automation usingML 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 whereML techniques can benefit.

Item Type: Article
Status: Published
DOI: https://doi.org/10.3390/s23218792
School/Department: School of Science, Technology and Health
URI: https://ray.yorksj.ac.uk/id/eprint/8980

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