Optimal Traffic Sharing in GERAN

Luna-Ramírez, Salvador; Toril, M.; Fernández-Navarro, Mariano; Wille, V.

doi:10.1007/s11277-009-9861-6

Cited by 8 publications

(4 citation statements)

References 47 publications

(74 reference statements)

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“…For simplicity, such a parameter is set to a fixed value, leading to inaccurate CR estimates in many cases. To solve these limitations, some authors use Voronoi tessellation [17,18] to define the polygon representing the service (or dominance) area for every site [14,[19][20][21][22]. Such a diagram can then be used to choose the nearest sites more accurately and, consequently, to estimate the ISD [21].…”

Section: Introductionmentioning

confidence: 99%

A Geometric Method for Estimating the Nominal Cell Range in Cellular Networks

García

Buenestado

Toril

et al. 2018

Mobile Information Systems

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In cellular networks, cell range is a key parameter for network planning and optimization. With the advent of new radio access technologies, it is not easy to obtain a good estimate of the nominal cell range on a cell-by-cell basis due to the complexity of physical layout in a real network. In this work, a novel geometrical method for estimating the cell range based on Voronoi tessellation is presented. The inputs of the method are site locations, antenna azimuths, and antenna horizontal beamwidths. The method is tested with a real dataset taken from a live LTE network. During assessment, the proposed method is compared with traditional approaches of estimating cell range. Results show that the proposed method improves the accuracy of previous approaches, while still maintaining a low computational complexity.

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Section: Introductionmentioning

confidence: 99%

A Geometric Method for Estimating the Nominal Cell Range in Cellular Networks

García

Buenestado

Toril

et al. 2018

Mobile Information Systems

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“…Both effects might have a strong impact on individual user QoE. The above tuning problem is a large-scale non-separable optimization problem [9]. In this work, the figure of merit is the overall system QoE, defined as:…”

Section: Problem Formulationmentioning

confidence: 99%

“…The aim of traffic steering is to alleviate the negative effects of uneven traffic demand distribution by sharing traffic between adjacent cells. To this end, different objectives can be defined, amongst which is to balance some network indicator across the network (e.g., average PRB utilization [6] [7] or call blocking ratio [8]) or maximize some overall network performance figure (e.g., total blocked traffic [9] or utility function based on individual cell loads [10]). Likewise, cell re-sizing for traffic steering can be achieved by changing physical parameters (e.g., base station transmit power [11] or antenna tilt angle [12] [13]) or logical parameters (e.g., cell reselection offset [14] or HandOver (HO) margin [15] [16]).…”

Section: Introductionmentioning

confidence: 99%

A Self-Tuning Algorithm for Optimal QoE-Driven Traffic Steering in LTE

et al. 2020

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Due to the wide diversity of services in mobile networks, cellular operators have changed their focus from Quality of Service (QoS) to Quality of Experience (QoE). To manage this change, Self-Organizing Networks (SON) techniques have been developed to automate network management, with traffic steering as a key use case. Traditionally, traffic steering aims to balance traffic volume or load among adjacent cells. Although more advanced schemes have been devised to balance QoE among cells, these do not guarantee that the overall system QoE is improved. In this work, a novel self-tuning algorithm for parameters in a classical mobility load balancing scheme is proposed to steer traffic among adjacent cells in a Long-Term Evolution (LTE) network driven by QoE criteria. Unlike previous approaches, based on heuristic rules, the proposed algorithm takes a gradient ascent approach to ensure that parameter changes always improve the overall system QoE. For this purpose, the impact of parameter changes on system QoE is estimated with an analytical network performance model that can be adjusted with statistics taken from the real network. The proposed algorithm is tested in a system-level simulator implementing a realistic LTE scenario. Results show that the method outperforms classical load and QoE mobility load balancing schemes. INDEX TERMS Long Term Evolution (LTE), self organizing network (SON), self-tuning, quality of experience.

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“…Thus, the goodness of tuning is usually assessed based on convergence speed and stability issues [31]. Only in very rare cases, the optimality conditions can be reformulated as a control problem (e.g., [32], where the problem of traffic sharing in GSM is formulated as a balancing problem between adjacent cells).…”

Section: Related Workmentioning

confidence: 99%

Self-Planning of Base Station Transmit Power for Coverage and Capacity Optimization in LTE

Buenestado

Toril

Luna-Ramírez

et al. 2017

Mobile Information Systems

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A computationally efficient self-planning algorithm for adjusting base station transmit power in a LTE system on a cell-by-cell basis is presented. The aim of the algorithm is to improve the overall network spectral efficiency in the downlink by reducing the transmit power of specific cells to eliminate interference problems. The main driver of the algorithm is a new indicator that predicts the impact of changes in the transmit power of individual cells on the overall network Signal to Interference plus Noise Ratio (SINR) for the downlink. Algorithm assessment is carried out over a static system-level simulator implementing a live LTE network scenario. During assessment, the proposed algorithm is compared with a state-of-the-art self-planning algorithm based on the modification of antenna tilt angles. Results show that the proposed algorithm can improve both network coverage and capacity significantly compared to other automatic planning methods.

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Optimal Traffic Sharing in GERAN

Cited by 8 publications

References 47 publications

A Geometric Method for Estimating the Nominal Cell Range in Cellular Networks

A Geometric Method for Estimating the Nominal Cell Range in Cellular Networks

A Self-Tuning Algorithm for Optimal QoE-Driven Traffic Steering in LTE

Self-Planning of Base Station Transmit Power for Coverage and Capacity Optimization in LTE

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