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 new, automated troubleshooting
methodology.