Context based configuration management of plug &amp;#x00026; play LTE base stations

Sanneck, Henning; Bouwen, Yves; Troch, Eddy

doi:10.1109/noms.2010.5488334

Cited by 11 publications

(6 citation statements)

References 2 publications

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“…Other tecniques, such as [84], [250], [251] aim to adjust the radio parameters based on measurements and data gathered from its neighbors. On [250], for example, the authors propose a framework for self-configuration of a LTE BS, in which a subset of BS parameters was assigned dynamically.…”

Section: Radio Parameters Configurationmentioning

confidence: 99%

“…On [250], for example, the authors propose a framework for self-configuration of a LTE BS, in which a subset of BS parameters was assigned dynamically. The solution proposed, Dynamic Radio Configuration Function (DRCF), assesses the coverage area of its neighbors in order to determine the best parameters of the new BS, form cell clusters and provide Tracking Area Codes (TAC) based on neighboring cells.…”

Section: Radio Parameters Configurationmentioning

confidence: 99%

“…In these works the authors used QL to intelligently associate users with different requirements, in terms of capacity, latency and resilience, to small cells depending on the backhaul connection that they offered. If the backhaul and the user needs match, then the user would be allocated to that cell, otherwise a new cell Miscellaneous - [6], [223], [248] [249] [84], [250], [251] is searched. Results showed that the proposed solutions were able to achieve better QoE for all users at the cost of a small decrease in total throughput.…”

Section: A Backhaulmentioning

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

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: Radio Parameters Configurationmentioning

confidence: 99%

Section: Radio Parameters Configurationmentioning

confidence: 99%

Section: A Backhaulmentioning

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

View full text Add to dashboard Cite

show abstract

“…The authors in [51] give a detailed classification of groups and stages in the initial configuration phase. They present a policy based framework for self configuration and propose that a subset of parameters can be configured in real time through a Dynamic Radio Configuration Function (DRCF).…”

Section: Effect Of Operators Policy On Self Configurationmentioning

confidence: 99%

A Survey of Self Organisation in Future Cellular Networks

Aliu

Imran

et al. 2013

IEEE Commun. Surv. Tutorials

379

217

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This article surveys the literature over the period of the last decade on the emerging field of self organisation as applied to wireless cellular communication networks. Self organisation has been extensively studied and applied in adhoc networks, wireless sensor networks and autonomic computer networks; however in the context of wireless cellular networks, this is the first attempt to put in perspective the various efforts in form of a tutorial/survey. We provide a comprehensive survey of the existing literature, projects and standards in self organising cellular networks. Additionally, we also aim to present a clear understanding of this active research area, identifying a clear taxonomy and guidelines for design of self organising mechanisms. We compare strength and weakness of existing solutions and highlight the key research areas for further development. This paper serves as a guide and a starting point for anyone willing to delve into research on self organisation in wireless cellular communication networks.

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“…These three phases commonly correspond to Self-configuration, Self-optimization, and Self-healing, which are also referred to as Self-X functionalities [3,20,21]. During the standardization process for LTE SON, 3GPP has defined a set of use cases and associated functions in Releases 9, 10 and 11 [22,23,24] which are described in Fig. 1.2.…”

Section: Self-organizing Functionalitiesmentioning

confidence: 99%