System for automated diagnosis in cellular networks based on performance indicators

Barco, Raquel; Wille, V.; Díez, Luis

doi:10.1002/ett.1060

Cited by 60 publications

(43 citation statements)

References 20 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%

“…This assumption leads to the creation of Naive Bayes' Classifiers. Recent research has applied the concept of Bayes' classifiers in fault detection [45], and fault classification [37], [38]. For interested readers, a more in-depth review of Bayesian classifiers, its advantages and disadvantages, and its two models, can be found in [46], [47].…”

Section: A Supervised Learningmentioning

confidence: 99%

“…In [37], for example, the authors present a system based on simple naive Bayesian classifiers in order to perform fault classification. The proposed framework focuses on troubleshooting RAN problems in 2G networks, but also addresses the issue of how the problems can be solved.…”

Section: B Fault Classificationmentioning

confidence: 99%

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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: A Supervised Learningmentioning

confidence: 99%

Section: A Supervised Learningmentioning

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

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show abstract

“…Once a and b in (14) are obtained, the grey differential equation can be used to predict the value of r u at time instant c + 1. The solution of r …”

Section: B Outage Detection Phasementioning

confidence: 99%

A Cell Outage Management Framework for Dense Heterogeneous Networks

Onireti

Zoha

Moysen

et al. 2016

IEEE Trans. Veh. Technol.

106

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Abstract-In this paper, we present a novel cell outage management (COM) framework for heterogeneous networks (HetNets) with split control and data planes -a candidate architecture for meeting future capacity, quality of service and energy efficiency demands. In such architecture, the control and data functionalities are not necessarily handled by the same node. The control base stations (BSs) manage the transmission of control information and user equipment (UE) mobility, while the data BSs handle UE data. An implication of this split architecture is that, an outage to a BS in one plane has to be compensated by other BSs in the same plane. Our COM framework addresses this challenge by incorporating two distinct cell outage detection (COD) algorithms to cope with the idiosyncrasies of both the data and control planes. The COD algorithm for control cells leverages the relatively larger number of UEs in the control cell to gather large scale minimize drive testing (MDT) reports data, and detects outage by applying machine learning and anomaly detection techniques. To improve outage detection accuracy, we also investigate and compare the performance of two anomaly detecting algorithms, i.e. k− nearest neighbor and local outlier factor based anomaly detector, within the control COD. On the other hand, for data cells COD, we propose a heuristic grey-prediction based approach, which can work with the small number of UEs in the data cell, by exploiting the fact that the control BS manages UE-data BS connectivity, by receiving a periodic update of the received signal reference power (RSRP) statistic between the UEs and data BSs in its coverage. The detection accuracy of the heuristic data COD algorithm is further improved by exploiting the Fourier series of residual error that is inherent to grey prediction model. Our COM framework integrates these two COD algorithms with a cell outage compensation (COC) algorithm which can be applied to both planes. Our COC solution utilizes an actor critic (AC) based reinforcement learning (RL) algorithm, which optimizes the capacity and coverage of the identified outage zone in a plane, by adjusting the antenna gain and transmission power of the surrounding BSs in that plane. The simulation results show that the proposed framework can detect both data and control cell outage, and also compensate for the detected outage in a reliable manner.

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“…A special case of SC has been examined in which a cell becomes catatonic (i.e., no service is available) due to bidirectional antenna gain failure, which may occur due to the malfunctioning of transmitting and receiving modules in Evolved Universal Terrestrial Radio Access (E-UTRA) NodeB (eNB). The reported studies in literature that addressed the problem of cell outage detection are either based on quantitative models [2] which requires domain expert knowledge, or simply rely on performance deviation metrics for detection [3]. Just recently interest has emerged in applying methods from the machine learning domain such as clustering algorithms [4] as well as Bayesian Networks [5] to automate the detection of faulty cell behavior.…”

Section: Introductionmentioning

confidence: 99%

A SON solution for sleeping cell detection using low-dimensional embedding of MDT measurements

Zoha

Saeed

Imran

et al. 2014

2014 IEEE 25th Annual International Symposium on Personal, Indoor, and Mobile Radio Communication (PIMRC)

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Abstract-Automatic detection of cells which are in outage has been identified as one of the key use cases for Self Organizing Networks (SON) for emerging and future generations of cellular systems. A special case of cell outage, referred to as Sleeping Cell (SC) remains particularly challenging to detect in state of the art SON because in this case cell goes into outage or may perform poorly without triggering an alarm for Operation and Maintenance (O&M) entity. Consequently, no SON compensation function can be launched unless SC situation is detected via drive tests or through complaints registered by the affected customers. In this paper, we present a novel solution to address this problem that makes use of minimization of drive test (MDT) measurements recently standardized by 3GPP and NGMN. To overcome the processing complexity challenge, the MDT measurements are projected to a low-dimensional space using multidimensional scaling method. Then we apply state of the art k-nearest neighbor and local outlier factor based anomaly detection models together with pre-processed MDT measurements to profile the network behaviour and to detect SC. Our numerical results show that our proposed solution can automate the SC detection process with 93% accuracy.

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System for automated diagnosis in cellular networks based on performance indicators

Cited by 60 publications

References 20 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 Cell Outage Management Framework for Dense Heterogeneous Networks

A SON solution for sleeping cell detection using low-dimensional embedding of MDT measurements

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