Troubleshooting of 3G LTE mobility parameters using iterative statistical model refinement

This paper presents a new troubleshooting methodology for 3G Long Term Evolution (LTE) networks based on a closed-form expression between Radio Resource Management (RRM) and Key Performance Indicator (KPI) parameters, using statistical learning. This methodology aims at locally optimising the RRM parameters of the cells with poor performance in an iterative manner. The optimization engine uses the closed-form relationship to calculate the optimized RRM parameters for these cells. The main advantage of this methodolgy is the small number of iterations required to achieve convergence and the QoS objective. A troubleshooting application scenario involving mobility in LTE networks is considered. Numerical simulations illustrate the benefits of our proposed scheme.