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

Research output: Contribution to Journal/Magazine › Journal article › peer-review

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Recent Advances in Machine Learning for Network Automation in the O-RAN. / Q. Hamdan, Mutasem ; Lee , Haeyoung ; Triantafyllopoulou, Dionysia et al.
In: Sensors, Vol. 23, No. 21, 8792, 28.10.2023.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Q. Hamdan, M, Lee , H, Triantafyllopoulou, D, Borralho, R, Kose, A, Amiri, E, Mulvey, D, Yu, W, Zitouni, R, Pozza, R, Hunt, B, Bagheri, H, Foh, CH, Heliot, F, Chen, G, Xiao, P, Wang, N & Tafazolli, R 2023, 'Recent Advances in Machine Learning for Network Automation in the O-RAN', Sensors, vol. 23, no. 21, 8792. https://doi.org/10.3390/s23218792

APA

Q. Hamdan, M., Lee , H., Triantafyllopoulou, D., Borralho, R., Kose, A., Amiri, E., Mulvey, D., Yu, W., Zitouni, R., Pozza, R., Hunt, B., Bagheri, H., Foh, C. H., Heliot, F., Chen, G., Xiao, P., Wang, N., & Tafazolli, R. (2023). Recent Advances in Machine Learning for Network Automation in the O-RAN. Sensors, 23(21), Article 8792. https://doi.org/10.3390/s23218792

Vancouver

Q. Hamdan M, Lee H, Triantafyllopoulou D, Borralho R, Kose A, Amiri E et al. Recent Advances in Machine Learning for Network Automation in the O-RAN. Sensors. 2023 Oct 28;23(21):8792. doi: 10.3390/s23218792

Author

Q. Hamdan, Mutasem ; Lee , Haeyoung ; Triantafyllopoulou, Dionysia et al. / Recent Advances in Machine Learning for Network Automation in the O-RAN. In: Sensors. 2023 ; Vol. 23, No. 21.

Bibtex

@article{ffdafaee46284d3d9fbecd530830c1b4,
title = "Recent Advances in Machine Learning for Network Automation in the O-RAN",
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 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.",
keywords = "open radio access networks, machine learning, artificial intelligence",
author = "{Q. Hamdan}, Mutasem and Haeyoung Lee and Dionysia Triantafyllopoulou and R{\'u}ben Borralho and Abdulkadir Kose and Esmaeil Amiri and David Mulvey and Wenjuan Yu and Rafik Zitouni and Riccardo Pozza and Bernie Hunt and Hamidreza Bagheri and Foh, {Chuan Heng} and Fabien Heliot and Gaojie Chen and Pei Xiao and Ning Wang and Rahim Tafazolli",
year = "2023",
month = oct,
day = "28",
doi = "10.3390/s23218792",
language = "English",
volume = "23",
journal = "Sensors",
issn = "1424-8220",
publisher = "Multidisciplinary Digital Publishing Institute (MDPI)",
number = "21",

}

RIS

TY - JOUR

T1 - Recent Advances in Machine Learning for Network Automation in the O-RAN

AU - Q. Hamdan, Mutasem

AU - Lee , Haeyoung

AU - Triantafyllopoulou, Dionysia

AU - Borralho, Rúben

AU - Kose, Abdulkadir

AU - Amiri, Esmaeil

AU - Mulvey, David

AU - Yu, Wenjuan

AU - Zitouni, Rafik

AU - Pozza, Riccardo

AU - Hunt, Bernie

AU - Bagheri, Hamidreza

AU - Foh, Chuan Heng

AU - Heliot, Fabien

AU - Chen, Gaojie

AU - Xiao, Pei

AU - Wang, Ning

AU - Tafazolli, Rahim

PY - 2023/10/28

Y1 - 2023/10/28

N2 - 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.

AB - 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.

KW - open radio access networks

KW - machine learning

KW - artificial intelligence

U2 - 10.3390/s23218792

DO - 10.3390/s23218792

M3 - Journal article

VL - 23

JO - Sensors

JF - Sensors

SN - 1424-8220

IS - 21

M1 - 8792

ER -