Cell Outage Compensation in LTE Networks: Algorithms and Performance Assessment

Amirijoo, Mehdi; Jorguseski, L.; Litjens, Remco; Schmelz, Lars Christoph

doi:10.1109/vetecs.2011.5956497

Cited by 36 publications

(23 citation statements)

References 3 publications

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“…These controllers can be found in all domains of SON, and its applications include but are not limited to: NCL configuration [118]- [120], radio parameters configuration [121]- [123], coverage and capacity optimization [122]- [130], HO optimization [131]- [144], load balancing [145], [146], resource optimization [123], [147]- [149], coordination of SON functions [150]- [152], fault detection [153] and cell outage management [154]- [161].…”

Section: Controllersmentioning

confidence: 99%

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: Controllersmentioning

confidence: 99%

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

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“…Specifically, the problem that we address is automatic cell anomaly detection. Typically, research has focused only on Cell-Outage Detection (COD) [2] and Cell-Outage Compensation (COC) [3] concepts, but, more recently, detection of general anomalies has also been addressed [4]. This paper addresses both the outage case and the case where the cell can provide a certain level of service, but its performance has degraded to a point below an expected tolerable level and directly impacts users' experience.…”

Section: A Self-healing For Sonmentioning

confidence: 99%

“…Previous research addressed the cell-outage detection [2] and cell-outage compensation [3] concepts. For the problem of cell-outage detection, Mueller et al [2] proposed a detection mechanism that uses Neighbor Cell List (NCL) reports.…”

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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“…A few algorithms have already been proposed in literature, e.g in [4], [13]- [18] and [8]- [11], for COD and COC, respectively. The COD problem has been addressed in [4] by leveraging the neighbor cell list (NCL) reports to construct a visibility graph, whose topology changes are used to detect cells that are experiencing outage.…”

Section: Introductionmentioning

confidence: 99%

“…Coluccia et al [17] analyzed the variations in the traffic profiles for 3G cellular systems to detect real world traffic anomalies. In terms of COC, the authors in [8], [9] investigated the effectiveness of control parameters such as the reference signal power, antenna tilt, scheduling parameters and the uplink target received power level in mitigating the effect of cell outage. In [10], the authors proposed an autonomic particle swarm compensation algorithm with fast convergence speed, which is a key requirement in COC.…”

Section: Introductionmentioning

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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Cell Outage Compensation in LTE Networks: Algorithms and Performance Assessment

Cited by 36 publications

References 3 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

Detecting anomalies in cellular networks using an ensemble method

A Cell Outage Management Framework for Dense Heterogeneous Networks

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