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Computer Science > Machine Learning

arXiv:1005.0340 (cs)
[Submitted on 3 May 2010]

Title:Statistical Learning in Automated Troubleshooting: Application to LTE Interference Mitigation

Authors:Moazzam Islam Tiwana, Berna Sayrac, Zwi Altman
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Abstract: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.
Comments: IEEE Transactions On Vehicular Technology 2010 IEEE transactions on vehicular technology
Subjects: Machine Learning (cs.LG)
Cite as: arXiv:1005.0340 [cs.LG]
  (or arXiv:1005.0340v1 [cs.LG] for this version)
  https://doi.org/10.48550/arXiv.1005.0340
Related DOI: https://doi.org/10.1109/TVT.2010.2050081

Submission history

From: Moazzam Tiwana [view email]
[v1] Mon, 3 May 2010 16:35:49 UTC (101 KB)
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