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2010
IEEE

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

9 years 1 months ago
Statistical Learning-based Automated Healing: Application to mobility in 3G LTE networks
Abstract--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 ne...
Moazzam Islam Tiwana, Berna Sayraç, Zwi Alt
Added 14 Feb 2011
Updated 14 Feb 2011
Type Journal
Year 2010
Where PIMRC
Authors Moazzam Islam Tiwana, Berna Sayraç, Zwi Altman, Tijani Chahed
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