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

[dietcha]

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.

http://www-public.int-evry.fr/~chahed/tiwana_pimrc2010.pdf

[rkdrkd]

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

This paper appears in:
Personal Indoor and Mobile Radio Communications (PIMRC), 2010 IEEE 21st International Symposium on
Date of Conference: 26-30 Sept. 2010
Author(s): Tiwana, M.I.
Orange Labs., RESA/NET, Issy-Les-Moulineaux, France
Sayrac, B. ; Altman, Z. ; Chahed, T.
Page(s): 1746 - 1751
Product Type: Conference Publications