Anomaly Detection in Multi Tiered Cellular Networks Using LSTM and 1D-CNN

Abstract Self Organizing Networks (SONs) are considered as one of the key features for automation of network management in new generation of mobile communications. The upcoming fifth generation (5G) mobile networks are likely to offer new advancements for SON solutions. In SON concept, self-healing is a prominent task which comes along with cell outage detection and cell outage compensation. 5G networks are supposed to have ultra-dense deployments which makes cell outage detection critical and harder for network maintenance. Therefore, by imitating the ultra-dense multi-tiered scenarios regarding 5G networks, this study investigates femtocell outage detection with the help of Long Short- Term Memory (LSTM) and one-dimensional Convolutional Neural Networks (1D-CNN) by means of time sequences of Key Performance Indicator (KPI) parameters generated in user equipments. In proposed scheme, probable anomalies in femto access points (FAP) are detected and classified within a predetermined time sequence intervals. On the average, in more than 80% of the cases the outage states of the femtocells are correctly predicted among healthyand anomalous states.