An Automatic Detection and Diagnosis Framework for Mobile Communication Systems

Szilágyi, Péter; Nováczki, Szabolcs

doi:10.1109/tnsm.2012.031912.110155

Cited by 89 publications

(57 citation statements)

References 19 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 [98], the authors develop an integrated framework for detection and diagnosis of anomalies in cellular networks. The detection is based on monitoring radio measurements and other KPIs and comparing them to their usual behavior while the diagnosis is based on reports of previous fault cases and learning their impact on different KPIs.…”

Section: A Fault Detectionmentioning

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

417

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: A Fault Detectionmentioning

confidence: 99%

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

Klaine

Imran

Onireti

et al. 2017

IEEE Commun. Surv. Tutorials

417

200

View full text Add to dashboard Cite

show abstract

“…In [26][27][28], empowering SONs with Big Data techniques is studied with the aim of transforming big data into a readily useable knowledge base. In the field of self-healing, several research efforts have been devoted to the development of usable automatic detection and diagnosis systems [29]. On the one hand, various mathematical approaches have been applied to analyze network measurements, such as Bayesian networks [30,31], Neural Networks [5,8], Fuzzy Logic combined with Genetic Algorithms [32], linear prediction [33], correlation [34], and statistical analysis [35,36].…”

Section: Related Workmentioning

confidence: 99%

A method for identifying faulty cells using a classification tree-based UE diagnosis in LTE

Muñoz

Barco

Cruz

et al. 2017

J Wireless Com Network

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The latest advances in wireless technologies have led to a proliferation of data mobile devices and services. As a consequence, mobile networks have experienced a significant increase in data traffic, while voice traffic has shown nearly no growth. It is therefore essential for operators to understand the data traffic behavior at the user level in order to ensure a good customer experience. In the radio access networks (RANs), traditional solutions based on cell-level measurements are not adequate to analyze performance of individual users. Instead, novel alternatives such as the use of call traces and the definition of new user-centric indicators will provide detailed and valuable information for each connection. One of the key measurements related to data services is the user throughput. In this work, the user throughput is adopted as the main attribute to conduct diagnosis in the RAN, which has typically been the bottleneck for data services. To that end, a binary classification tree is proposed to determine the root cause of poor throughput in user-level data sessions. Then, this information is aggregated at the cell level in order to provide effective diagnosis of degraded cells. In particular, a correlation-based analysis of the cell status is proposed in order to identify abnormal cell behaviors in an automatic way. Evaluation has been carried out with datasets from live cellular networks. Results show that the proposed diagnosis approach is an effective means to identify the main factors that limit the user throughput in network cells.

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“…• proposing a new ensemble-method approach for cell anomaly detection that computes a numerical measure referred to as the KPI degradation level [5], to indicate the severity of the degradation,…”

Section: B Contributionsmentioning

confidence: 99%

“…More recently, detection of general anomalies has also been addressed [4], [5], [6]. However, to the best of our knowledge, our approach is the first to employ an adaptive ensemble method that copes with concept drift.…”

Section: Related Workmentioning

confidence: 99%

Detecting anomalies in cellular networks using an ensemble method

Ciocarlie

Lindqvist

Nováczki³

et al. 2013

Proceedings of the 9th International Conference on Network and Service Management (CNSM 2013)

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Abstract-The Self-Organizing Networks (SON) concept includes the functional area known as self-healing, which aims to automate the detection and diagnosis of, and recovery from, network degradations and outages. This paper focuses on the problem of cell anomaly detection, addressing partial and complete degradations in cell-service performance, and it proposes an adaptive ensemble method framework for modeling cell behavior. The framework uses Key Performance Indicators (KPIs) to determine cell-performance status and is able to cope with legitimate system changes (i.e., concept drift). The results, generated using real cellular network data, suggest that the proposed ensemble method automatically and significantly improves the detection quality over univariate and multivariate methods, while using intrinsic system knowledge to enhance performance.

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An Automatic Detection and Diagnosis Framework for Mobile Communication Systems

Cited by 89 publications

References 19 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

A method for identifying faulty cells using a classification tree-based UE diagnosis in LTE

Detecting anomalies in cellular networks using an ensemble method

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