Location accuracy impact on cell outage detection in LTE-A networks

Automated and timely detection of malfunctioning cells in Long-Term Evolution (LTE) networks is of high importance. Sleeping cell is a particular type of cell degradation hardly detectable by traditional network monitoring systems. Recent introduction of Minimization of Drive Test (MDT) functionality enables to collect user-level statistics from regular user devices without expensive and time-consuming drive-test and measurement campaigns. In this study data mining techniques are used to process MDT measurements to detect efficiently a sleeping cell. The developed earlier data mining framework is briefly overviewed in the paper. Special attention is devoted to post-processing stage as one of the key elements of the detection scheme. In practice, location information of collected measurements might contain considerable errors. This factor impacts the precision of malfunctioning cell detection. Therefore several post-processing algorithms are proposed, where location accuracy is taken into account. The performance of the algorithms is compared based on the results of thorough system-level LTE network simulations. Combined post-processing method shows the best reliability against location errors in terms of Root Mean Squared Error (RMSE) and percent gain.