Cell Outage Detection Based on Handover Statistics

de-la-Bandera, Isabel; Barco, Raquel; Muñoz, Pablo; Serrano, Inmaculada

doi:10.1109/lcomm.2015.2426187

Cited by 40 publications

(22 citation statements)

References 6 publications

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“…In cellular systems, anomaly detection algorithms are used mainly in self-healing to detect abnormal network behavior [97]- [102], [108], fault classification [98], [99], and perform cell outage management [9], [49], [50], [52], [70], [71], [104], [105], [107], [115], [116].…”

Section: B Unsupervised Learningmentioning

confidence: 99%

“…In [104], for example, the authors propose to detect outage via the analysis of HO statistics. In [105], Muñoz et al, apply a solution that detects degraded cells through the analysis of time evolution metrics.…”

Section: Cell Outage Managementmentioning

confidence: 99%

“…Another example can be in the self-healing domain, in which the whole network might be required to be monitored in order to detect and manage faults. [9], [49], [50], [52], [70], [71], [104], [105], [107], [115], [116] Misc.…”

Section: A Scalabilitymentioning

confidence: 99%

See 2 more Smart Citations

A Survey of Machine Learning Techniques Applied to Self-Organizing Cellular Networks

Klaine

Imran

Onireti

et al. 2017

IEEE Commun. Surv. Tutorials

412

200

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Abstract-In this paper, a survey of the literature of the past fifteen years involving Machine Learning (ML) algorithms applied to self organizing cellular networks is performed. In order for future networks to overcome the current limitations and address the issues of current cellular systems, it is clear that more intelligence needs to be deployed, so that a fully autonomous and flexible network can be enabled. This paper focuses on the learning perspective of Self Organizing Networks (SON) solutions and provides, not only an overview of the most common ML techniques encountered in cellular networks, but also manages to classify each paper in terms of its learning solution, while also giving some examples. The authors also classify each paper in terms of its self-organizing use-case and discuss how each proposed solution performed. In addition, a comparison between the most commonly found ML algorithms in terms of certain SON metrics is performed and general guidelines on when to choose each ML algorithm for each SON function are proposed. Lastly, this work also provides future research directions and new paradigms that the use of more robust and intelligent algorithms, together with data gathered by operators, can bring to the cellular networks domain and fully enable the concept of SON in the near future.

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Section: B Unsupervised Learningmentioning

confidence: 99%

Section: Cell Outage Managementmentioning

confidence: 99%

See 1 more Smart Citation

A Survey of Machine Learning Techniques Applied to Self-Organizing Cellular Networks

Klaine

Imran

Onireti

et al. 2017

IEEE Commun. Surv. Tutorials

412

200

View full text Add to dashboard Cite

show abstract

“…By modeling the most common behaviors, the machine selects the set of unusual events [114]. Self healing is one of the main functionality in which this kind of techniques are applied, some examples are [115], [116]. The two most common techniques are:…”

Section: B Unsupervised Learning (Ul)mentioning

confidence: 99%

From 4G to 5G: Self-organized network management meets machine learning

Moysen

Giupponi

2018

Computer Communications

170

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In this paper, we provide an analysis of selforganized network management, with an end-to-end perspective of the network. Self-organization as applied to cellular networks is usually referred to Self-organizing Networks (SONs), and it is a key driver for improving Operations, Administration, and Maintenance (OAM) activities. SON aims at reducing the cost of installation and management of 4G and future 5G networks, by simplifying operational tasks through the capability to configure, optimize and heal itself. To satisfy 5G network management requirements, this autonomous management vision has to be extended to the end to end network. In literature and also in some instances of products available in the market, Machine Learning (ML) has been identified as the key tool to implement autonomous adaptability and take advantage of experience when making decisions. In this paper, we survey how network management can significantly benefit from ML solutions. We review and provide the basic concepts and taxonomy for SON, network management and ML. We analyse the available state of the art in the literature, standardization, and in the market. We pay special attention to 3rd Generation Partnership Project (3GPP) evolution in the area of network management and to the data that can be extracted from 3GPP networks, in order to gain knowledge and experience in how the network is working, and improve network performance in a proactive way. Finally, we go through the main challenges associated with this line of research, in both 4G and in what 5G is getting designed, while identifying new directions for research.

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“…The authors in [3] use neighbor cell measurements and handover statistics to detect anomalies and outages that is based on the number of incoming handovers (inHO) measured on a per cell basis by neighboring cells. This approach monitors situations where the number of inHO becomes zero as a potential symptom of cell outage.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning for Qoe Prediction and Anomaly Detection in Self-Organizing Mobile Networking Systems

Murudkar¹,

Gitlin²

2019

IJWMN

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Existing mobile networking systems lack the level of intelligence, scalability, and autonomous adaptability required to optimally enable next-generation networks like 5G and beyond, which are expected to be Self-Organizing Networks (SONs). It is anticipated that machine learning (ML) will be instrumental in designing future "x"G SON networks with their demanding Quality of Experience (QoE) requirements. This paper evaluates a methodology that uses supervised machine learning to predict the QoE level of the end user experiences and uses this information to detect anomalous behavior of dysfunctional network nodes (eNodeBs/base stations) in self-organizing mobile networks. An end-to-end network scenario is created using the network simulator ns-3, where end users interact with a remote host that is accessed over the Internet to run the most commonly used applications like file downloads and uploads and the resulting output is used as a dataset to implement ML algorithms for QoE prediction and eNodeB (eNB) anomaly detection. Three ML algorithms were implemented and compared to study their effectiveness and the scalability of the methodology. In the test network, an accuracy score greater than 99% is achieved using the ML algorithms. As suggested by the ns-3 simulation the use of ML for QoE prediction will help network operators understand end-user needs and identify network elements that are failing and need attention and recovery.

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Cell Outage Detection Based on Handover Statistics

Cited by 40 publications

References 6 publications

A Survey of Machine Learning Techniques Applied to Self-Organizing Cellular Networks

A Survey of Machine Learning Techniques Applied to Self-Organizing Cellular Networks

From 4G to 5G: Self-organized network management meets machine learning

Machine Learning for Qoe Prediction and Anomaly Detection in Self-Organizing Mobile Networking Systems

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