Steered Optimization Strategy for Automatic Cell Planning of UMTS Networks

Jamaa, S. Ben; Altman, Zwi; Picard, Jean-Marc; Ortega, A.

doi:10.1109/vetecs.2005.1543311

Cited by 12 publications

(6 citation statements)

References 3 publications

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“…The next seven values are calculated iteratively by the SLAH algorithm during the optimization phase (Phase-II in the Table). The values of α j=14 , α j=15 , α j=22 , α j=23 and α j=43 are calculated using equation (4). In spite of the inherent noise present in the generated data, one can see from the values depicted in Phase-II that α s=45 converges in a few iterations.…”

Section: Resultsmentioning

confidence: 99%

“…In other words, the RRM parameter that is found as the fault cause by the diagnosis process is optimized by the automated healing module. Local type of optimization or "steered optimization" has been studied in the literature, based on combinatorial optimization in conjunction with the interference matrix to tackle local problems detected in the network [4]. This approach uses a network simulator and can be implemented as an advanced functionality of a cell planning tool.…”

Section: Introductionmentioning

confidence: 99%

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Statistical Learning in Automated Troubleshooting: Application to LTE Interference Mitigation

Tiwana¹,

Sayraç²,

Altman³

2010

IEEE Trans. Veh. Technol.

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This paper presents a method for automated healing as part of off-line automated troubleshooting. The method combines statistical learning with constraint optimization. The automated healing aims at locally optimizing radio resource management (RRM) or system parameters of cells with poor performance in an iterative manner. The statistical learning processes the data using Logistic Regression (LR) to extract closed form (functional) relations between Key Performance Indicators (KPIs) and Radio Resource Management (RRM) parameters. These functional relations are then processed by an optimization engine which proposes new parameter values. The advantage of the proposed formulation is the small number of iterations required by the automated healing method to converge, making it suitable for off-line implementation. The proposed method is applied to heal an Inter-Cell Interference Coordination (ICIC) process in a 3G Long Term Evolution (LTE) network which is based on soft-frequency reuse scheme. Numerical simulations illustrate the benefits of the proposed approach.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Statistical Learning in Automated Troubleshooting: Application to LTE Interference Mitigation

Tiwana¹,

Sayraç²,

Altman³

2010

IEEE Trans. Veh. Technol.

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“…Since capacity and coverage are somehow related, they use a multi-objective formulation. Finally, Jamaa et al [43] also propose a steered optimization strategy in order to solve local quality problems as well as answering the question of how local the optimization problem should be defined. They came to the conclusion that considering the problem too locally reduce the efficiency of planning tools and taking a too large set increase the number of required BSs thus, increasing the cost of the network.…”

Section: Radio Planning Algorithmsmentioning

confidence: 99%

Topological planning and design of UMTS mobile networks: a survey

St-Hilaire

2008

Wireless Communications

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In this paper, we present a literature review on the topological planning problem of third generation (3G) cellular networks based on the universal mobile telecommunications system (UMTS) standard. After describing the UMTS architecture, we introduce each subproblem and present major works that have been done. The cell, the access and the core network planning problems have all been considered as well as a more global approach (when more than one subproblems are considered simultaneously). Both planning and expansion algorithms are also included in this review. The goal of this paper is to present and classify the different research works that have been done so that it can be used as a starting point for future research on topological design of UMTS networks.• The optimal number of BSs.• The best location to install the BSs. • The type (or model) of BSs. Fig. 2. UMTS planning subproblems.

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“…The so-called steered optimization technique is one such method introduced in [3]. It uses an interference matrix approach to identify the most coupled eNodeBs (eNBs) with the problematic cell in a cellular (UMTS) environment.…”

Section: Introductionmentioning

confidence: 99%

Statistical Learning-based Automated Healing: Application to mobility in 3G LTE networks

Tiwana¹,

Sayraç²,

Altman³

et al. 2010

21st Annual IEEE International Symposium on Personal, Indoor and Mobile Radio Communications

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Troubleshooting of wireless networks is a challenging network management task. We have developed, in a previous work, a new troubleshooting methodology, which we named Statistical Learning Automated Healing (SLAH). This methodology uses statistical learning, in particular logistic regression, to extract the functional relationships between the noisy Key Performance Indicators (KPIs) and Radio Resource Management (RRM) parameters. These relationships are then processed by an optimization engine so as to calculate the optimized RRM parameters which improve the KPIs of a degraded cell. The process is iterative and converges to the optimum RRM parameter value in few iterations, which makes it suitable for wireless networks. The present work focuses on the adaptation of SLAH for troubleshooting the mobility parameter, namely the handover margin, in 3G Long Term Evolution (LTE) networks. The simulation results, which we obtain for a practical use case, show the advantage of this new, automated troubleshooting methodology.

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Steered Optimization Strategy for Automatic Cell Planning of UMTS Networks

Cited by 12 publications

References 3 publications

Statistical Learning in Automated Troubleshooting: Application to LTE Interference Mitigation

Statistical Learning in Automated Troubleshooting: Application to LTE Interference Mitigation

Topological planning and design of UMTS mobile networks: a survey

Statistical Learning-based Automated Healing: Application to mobility in 3G LTE networks

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