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

Q. Hamdan, Mutasem and Lee, Haeyoung and Triantafyllopoulou, Dionysia and Borralho, Rúben and Kose, Abdulkadir and Amiri, Esmaeil and Mulvey, David and Yu, Wenjuan and Zitouni, Rafik and Pozza, Riccardo and Hunt, Bernie and Bagheri, Hamidreza and Foh, Chuan Heng and Heliot, Fabien and Chen, Gaojie and Xiao, Pei and Wang, Ning and Tafazolli, Rahim (2023) Recent Advances in Machine Learning for Network Automation in the O-RAN. Sensors, 23 (21): 8792. ISSN 1424-8220

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.