Femtocell Outage Detection in Multi-Tiered Networks using LSTM

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 by means of the metrics generated in user equipments with the help of Long-Short Term Memory (LSTM). Based on the parameters such as signal to interference noise ratio and channel quality indicator, time series data of the user equipments in the femtocell site are trained and tested with LSTM network. On the average, in more than 77% of the cases the outage states of the femtocells are correctly predicted.