Automated Wireless Coverage Optimization With Controlled Overlap

Fagen, D.; Vicharelli, Pablo A.; Weitzen, Jay

doi:10.1109/tvt.2007.912329

Cited by 42 publications

(22 citation statements)

References 12 publications

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“…Other approaches, such as in [122], [126], [127], utilize feedback controllers in order to optimize the coverage and capacity of the network. Claussen, et al, in [122], develop a coverage adaptation mechanism for femtocell deployments that utilizes information about mobility events of passing-by and indoor users to optimize femtocell coverage.…”

Section: Coverage and Capacitymentioning

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

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: Coverage and Capacitymentioning

confidence: 99%

“…Fagen et al, in [126], propose a method to simultaneously maximize coverage while minimizing the interference for a desired level of coverage overlap. This optimization can be done for individual BS, a cluster of BSs or the whole network.…”

Section: Coverage and Capacitymentioning

confidence: 99%

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

“…Much of the previous work [6][7][8] has focused on minimising the transmitting power of BS. Fagen et al [9] proposed a new automated method of simultaneously maximising coverage while minimising interference for a desired level of coverage overlap. However, such an approach is not always practical as network optimisation is constrained by a number of restrictions on BS placements, interference and power emissions.…”

Section: Review Of Existing Workmentioning

confidence: 99%

“…Equations (14), (19) and (21) complete the expression of the mean capacity of the network with respect to the position of the FAP b as shown in Equation (9).…”

Section: Formulation Scenariomentioning

confidence: 99%

Low energy indoor network: deployment optimisation

Wang

Guo

O’Farrell

2012

J Wireless Com Network

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This article considers what the minimum energy indoor access point deployment is in order to achieve a certain downlink quality-of-service. The article investigates two conventional multiple-access technologies, namely: LTEfemtocells and 802.11n Wi-Fi. This is done in a dynamic multi-user and multi-cell interference network. Our baseline results are reinforced by novel theoretical expressions. Furthermore, the work underlines the importance of considering optimisation when accounting for the capacity saturation of realistic modulation and coding schemes. The results in this article show that optimising the location of access points both within a building and within the individual rooms is critical to minimise the energy consumption.

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“…The various link budget parameters shown in [3] are applied as input factors to the path loss models, then useful information can be extracted for network deployment, such as the location of cell sites required to cover the target area. Some of these parameters are within the control of the designer, such as transmit (Tx) power levels, antenna orientation, or tilt [4]. By manipulating these parameters, network operators try to optimize wireless cell coverage.…”

Section: Introductionmentioning

confidence: 99%

Cell coverage estimation by radio fingerprint data analytics

Kim

Yeo

2014

J Wireless Com Network

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We develop a novel cell coverage estimation method based on radio fingerprints collected from practical wireless service systems. A large size radio fingerprint map that shows cell identifiers and signal strength measurements on grid segmentation is built for effective estimation of cell coverage. An essential part of cell coverage estimation is radio fingerprint data cleansing and compensation. Based on this proposed iterative fingerprint data analysis method, we detect the proper cell borderline for each cell site. By the proposed method, we can efficiently estimate the cell coverage of each cell site without difficult manual field measurements. Moreover, mobile service providers can economically plan network configuration and manage subscribers using these advances in cell coverage estimation.

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Automated Wireless Coverage Optimization With Controlled Overlap

Cited by 42 publications

References 12 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

Low energy indoor network: deployment optimisation

Cell coverage estimation by radio fingerprint data analytics

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