The influence of dataset size on the performance of cell outage detection approach in LTE-A networks

The configuration and maintenance of constantly evolving mobile cellular networks are getting more and more complex and hence expensive. Self-Organizing Networks (SON) concept is an umbrella term for the set of automated solutions for network operations proposed by 3rd Generation Partnership Project (3GPP) group. Automated cell outage detection is one of the components of SON functionality. In early studies our research group developed data-driven approach for the detection of malfunctioning cells. In this paper we investigate the performance of the proposed solution as a function of the density of active users and the size of observation interval. The evaluation is conducted in Long Term Evolution (LTE)/LTE-Advanced (LTE-A) system level simulator. The analyzed data is the collection of Minimization of Drive Testing (MDT) reports, which is basically user-level statistics. Our approach is able to detect cells experiencing random access failures, but the performance depends on the amount of available data.