Statistical Learning in Automated Troubleshooting: Application to LTE Interference Mitigation

TL;DR

This paper introduces a statistical learning-based automated troubleshooting method for LTE networks that efficiently optimizes radio parameters to mitigate interference, combining logistic regression with constraint optimization for rapid convergence.

Contribution

It presents a novel off-line automated healing approach that integrates statistical learning and constraint optimization to improve LTE interference mitigation.

Findings

Reduces the number of iterations needed for convergence.

Improves LTE network performance through automated parameter tuning.

Demonstrates effectiveness via numerical simulations.

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.