Improved Neural Network Transparency for Cell Degradation Detection Using Explanatory Model

Mulvey, David; Foh, Chuan Heng; Imran, Muhammad Ali; Tafazolli, Rahim

doi:10.1109/icc40277.2020.9149391

Cited by 2 publications

(2 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Recently, there has also been growing interest in federated learning [67], which can potentially harmonize data across multiple site locations, allowing a centralized ML system to utilize data from a large number of locations, when translated into a common format.…”

Section: Proactive Maintenancementioning

confidence: 99%

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

Hamdan,

Lee,

Triantafyllopoulou

et al. 2023

Sensors

View full text Add to dashboard Cite

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.

show abstract

Section: Proactive Maintenancementioning

confidence: 99%

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

Hamdan,

Lee,

Triantafyllopoulou

et al. 2023

Sensors

View full text Add to dashboard Cite

show abstract

“…Thus, efforts are needed not only to design new AI/ML models, but also to explain the working principle of the models. Fortunately, such research works are slowly emerging, for example, that of [25], which develops a parallel model to explain the behaviour of a recurrent neural network (RNN).…”

mentioning

confidence: 99%

Trends in Intelligent Communication Systems: Review of Standards, Major Research Projects, and Identification of Research Gaps

Koufos

Haloui

Dianati

et al. 2021

JSAN

Self Cite

View full text Add to dashboard Cite

The increasing complexity of communication systems, following the advent of heterogeneous technologies, services and use cases with diverse technical requirements, provide a strong case for the use of artificial intelligence (AI) and data-driven machine learning (ML) techniques in studying, designing and operating emerging communication networks. At the same time, the access and ability to process large volumes of network data can unleash the full potential of a network orchestrated by AI/ML to optimise the usage of available resources while keeping both CapEx and OpEx low. Driven by these new opportunities, the ongoing standardisation activities indicate strong interest to reap the benefits of incorporating AI and ML techniques in communication networks. For instance, 3GPP has introduced the network data analytics function (NWDAF) at the 5G core network for the control and management of network slices, and for providing predictive analytics, or statistics, about past events to other network functions, leveraging AI/ML and big data analytics. Likewise, at the radio access network (RAN), the O-RAN Alliance has already defined an architecture to infuse intelligence into the RAN, where closed-loop control models are classified based on their operational timescale, i.e., real-time, near real-time, and non-real-time RAN intelligent control (RIC). Different from the existing related surveys, in this review article, we group the major research studies in the design of model-aided ML-based transceivers following the breakdown suggested by the O-RAN Alliance. At the core and the edge networks, we review the ongoing standardisation activities in intelligent networking and the existing works cognisant of the architecture recommended by 3GPP and ETSI. We also review the existing trends in ML algorithms running on low-power micro-controller units, known as TinyML. We conclude with a summary of recent and currently funded projects on intelligent communications and networking. This review reveals that the telecommunication industry and standardisation bodies have been mostly focused on non-real-time RIC, data analytics at the core and the edge, AI-based network slicing, and vendor inter-operability issues, whereas most recent academic research has focused on real-time RIC. In addition, intelligent radio resource management and aspects of intelligent control of the propagation channel using reflecting intelligent surfaces have captured the attention of ongoing research projects.

show abstract

Improved Neural Network Transparency for Cell Degradation Detection Using Explanatory Model

Cited by 2 publications

References 3 publications

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

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

Trends in Intelligent Communication Systems: Review of Standards, Major Research Projects, and Identification of Research Gaps

Contact Info

Product

Resources

About