A Mathematical Perspective of Self-Optimizing Wireless Networks

Viering, Ingo; Döttling, M.; Lobinger, Andreas

doi:10.1109/icc.2009.5198628

Cited by 164 publications

(71 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

410

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

410

200

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“…The tool has been used in several standardization and research studies, such as [3], [11]. Additional simulator modeling can be found in [12]. A total of 630 users are dropped in the scenario, split into slow-and high-speed users.…”

Section: Scenario and Simulation Methodologymentioning

confidence: 99%

UE autonomous cell management in a high-speed scenario with dual connectivity

Gimenez

Michaelsen²,

Pedersen

2016

2016 IEEE 27th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC)

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Abstract-This study compares the amount of control signaling required by traditional network-controlled mobility management with the one required by user equipment autonomous cell management operations in a real-life highway scenario. The scenario is covered by macros and densely-deployed small cells. Different strategies for preparing the small cells for autonomous operations are studied. Our results show that traditional dual connectivity requires an average of 4.9 messages, per user per second, to be exchanged between the user equipment and the network, and 11.6 messages between e-NodeBs. On the other hand, autonomous cell management operations considerably decrease the amount of signaling. The highest reductions can be achieved by preparing all cells along the highway, cutting the signaling overhead by 92 % over the air, and 39 % between e-NodeBs. Furthermore, the approach of applying a newly developed window-based feature for preparing the cells brings significant benefits.

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“…resource efficiency, if PRB m in cell i is allocated to user k .Constraint in (5) which is derived by (2), (3) and (4) represents that the available data rate of any user in its current serving cell must achieve its data requirement. P BS and P sub in constraints (6) and (7) are the total power limitation at the eNB and each PRB, respectively.…”

Section: Problem Formulationmentioning

confidence: 99%

“…A mathematic perspective of self-optimizing wireless network is proposed in [2], which introduces a mathematical model of load balancing. The cell load is defined by the ratio of the required physical resource blocks (PRB) and the available total PRBs in one cell [3].…”

Section: Introductionmentioning

confidence: 99%