An Approach for Network Outage Detection from Drive-Testing Databases

Turkka, Jussi; Chernogorov, Fedor; Brigatti, Kimmo; Ristaniemi, Tapani; Lempiainen, Jukka

doi:10.1155/2012/163184

Cited by 28 publications

(20 citation statements)

References 9 publications

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“…In the context of cellular networks, supervised learning can be applied in several domains, such as: mobility prediction [27]- [30], resource allocation [31]- [33], load balancing [34], HO optimization [35], [36], fault classification [37], [38] and cell outage management [39]- [42] Supervised learning is a very broad domain and has several learning algorithms, each with their own specifications and applications. In the following, the most common algorithms applied in the context of cellular networks are presented.…”

Section: A Supervised Learningmentioning

confidence: 99%

“…In SON applications, the most popular dimension reduction techniques are Principal Component Analysis (PCA) [42], [107], [183], Minor Component Analysis (MCA) [42], [103], Diffusion Maps (DM) [39], [241] and MultiDimensional Scaling (MDS) [9], [49], [50], [70], [71].…”

Section: G Dimension Reductionmentioning

confidence: 99%

“…Turkka et al, in [39], build a data-mining framework that is able to detect sleeping cells, network outage and change of dominance areas. The main idea consists of finding similarities between periodical network measurements and previously known outage data.…”

Section: Cell Outage Managementmentioning

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

416

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: A Supervised Learningmentioning

confidence: 99%

Section: G Dimension Reductionmentioning

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

416

200

View full text Add to dashboard Cite

show abstract

“…On the other hand, there are some works in the literature that investigate the problem of cell outage by employing user-centric measurements [39][40][41]. Nevertheless, this information is commonly related to the radio environment (e.g., signal strength) derived from MDT functionality, while other measurements related to integrity performance such as user throughput are ignored.…”

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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“…A method using the operational data from user equipment and different network elements is proposed to overcome the challenges and limitations of manual drive testing [12]. Under the paradigm of big data, the probability of subscriber churn is supposed to be well predicted because the intersubscriber influence is adopted [13].…”

Section: Mobile Information Systemsmentioning

confidence: 99%

A Data-Driven Customer Quality of Experience System for a Cellular Network

Jung

Mo²,

Park

2017

Mobile Information Systems

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Improving customer-perceived service quality is a critical mission of telecommunication service providers. Using 35 billion call records, we develop a call quality score model to predict customer complaint calls. The score model consists of two components: service quality score and connectivity score models. It also incorporates human psychological impacts such as the peak and end effects. We implement a large-sized data processing system that manages real-time service logs to generate quality scores at the customer level using big data processing technology and analysis techniques. The experimental results confirm the validity of the developed model in distinguishing probable complaint callers. With the adoption of the system, the first call resolution rate of the call center increased from 45% to 73%, and the field engineer dispatch rate from 46% to 25%.

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An Approach for Network Outage Detection from Drive-Testing Databases

Cited by 28 publications

References 9 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

A Data-Driven Customer Quality of Experience System for a Cellular Network

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