  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.
