An Automatic Procedure for Neighbor Cell List Definition in Cellular Networks

Parodi, Flavio; Kylväjä, Mikko; Alford, Gordon; Li, Juan; Pradas, José María Vicente

doi:10.1109/wowmom.2007.4351742

Cited by 31 publications

(27 citation statements)

References 1 publication

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“…In solutions such as [119], [120], the authors apply an automatic procedure of NCL configuration and update by ranking the neighbor cells of the newly deployed BS according to certain parameters, such as coverage overlap or number of HO. After this process is done, a list is built based on them and the neighbors are obtained.…”

Section: B Neighbor Cell List (Ncl) Configurationmentioning

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: B Neighbor Cell List (Ncl) Configurationmentioning

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

show abstract

“…In [22], the use of mobile measurement to update the NCL of macrocells after deployment has been studied: since the intrafrequency reporting function, known as Detected Set Reporting (DSR), is energetically costly for the mobile device, the use of it is suggested only in critical situations where a problem with the current NCL is known. A similar case, where the NCL needs to be updated when a new macrocell is deployed, is studied in [23]. Other ways to dynamically build the NCL via crowdsourcing are presented in [18,24].…”

Section: Related Workmentioning

confidence: 99%

“…In this way, whenever a macrocell detects a problem, or when a new cell is deployed [23], such a function can be activated. The only disadvantage is that such functionality is energetically costly for the mobile device, so its use is recommended for short periods of time and only in critical situations where a problem with the current NCL is known.…”

Section: G Network Optimisationmentioning

confidence: 99%

Updating Neighbour Cell List via Crowdsourced User Reports: A Framework for Measuring Time Performance

Checco

Lancia

Leith

2018

Wireless Communications and Mobile Computing

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In modern wireless networks deployments, each serving node needs to keep its Neighbour Cell List (NCL) constantly up to date to keep track of network changes. The time needed by each serving node to update its NCL is an important parameter of the network's reliability and performance. An adequate estimate of such parameter enables a significant improvement of self-configuration functionalities. This paper focuses on the update time of NCLs when an approach of crowdsourced user reports is adopted. In this setting, each user periodically reports to the serving node information about the set of nodes sensed by the user itself. We show that, by mapping the local topological structure of the network onto states of increasing knowledge, a crisp mathematical framework can be obtained, which allows in turn for the use of a variety of user mobility models. Further, using a simplified mobility model we show how to obtain useful upper bounds on the expected time for a serving node to gain Full Knowledge of its local neighbourhood.

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“…A general framework for self configuration of future LTE networks has been presented in [29]. This paper addresses the problems associated with autonomous deployment of a new site i.e.…”

Section: Self Configurationmentioning

confidence: 99%

“…Authors in [29] propose an algorithm for generating a neighbour cell list and updating it using a centralised as well as decentralised approach. The criteria for the selection of neighbours or the initial generation of the neighbour cell list can be based on the geographical coordinates of the cell sites as demonstrated in [32].…”

Section: B Neighbour Cell List 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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An Automatic Procedure for Neighbor Cell List Definition in Cellular Networks

Cited by 31 publications

References 1 publication

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

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

Updating Neighbour Cell List via Crowdsourced User Reports: A Framework for Measuring Time Performance

A Survey of Self Organisation in Future Cellular Networks

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