Anomaly detection in multi-tiered cellular networks using LSTM and 1D CNN

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 mobile networks and beyond 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. Next-generation cellular networks are supposed to have ultra-dense deployments which make cell outage detection critical and harder for network maintenance. Therefore, by imitating the ultra-dense multi-tiered scenarios, this study scrutinizes femtocell outage detection with the help of long short-term memory and one-dimensional convolutional neural networks by using time sequences of key performance indicator parameters generated in user equipment. In both the proposed schemes, probable outage-related anomalies in femto access points (FAP) are detected and classified within predetermined time sequence intervals. Moreover, aggregation decision methods are also incorporated into the proposed framework for boosting cell outage detection procedure on FAP level. Our findings show that proposed deep learning approaches outperform existing feed-forward neural networks, and on the average, in more than 80% of the cases the outage states of the femtocells are correctly predicted among healthy and three anomalous states.