Statistical Learning for Automated RRM: Application to eUTRAN Mobility

Self organizing network (SON) functionalities are currently developed to improve network performance and management tasks. SON functionalities require efficient utilization of data extracted from the network. In this context, the paper has two objectives. First it is shown that one can use simple statistical learning techniques such as regression to extract a model from data. The model comprises closed form expressions that approximate the functional relations between measured key performance indicators (KPIs) and radio resource management (RRM) parameters. Second, it is shown how the model can be integrated in a monitoring process and be used to devise an efficient auto-tuning algorithm. To this end, two case studies of handover monitoring and handover auto-tuning in a LTE network are described and illustrate the application of the proposed approach.