Communication supervision function for verticals in 4G networks and beyond: Traffic anomaly detection from aggregated LTE MAC layer reports using a LSTM-RNN

Abstract

We study the feasibility of developing a Communication Supervision Function for a 4G LTE wireless communications network system, to allow a vertical to monitor from its domain the Quality of Service (QoS) of the communication traversing the wireless domain. Communication supervision is performed by detecting traffic anomalies of a reference, healthy, transmission of packets uniformly spaced at intervals with ms resolution, and transmitted in uplink direction. Traffic at the base station is monitored with a LTE Medium Access Control (MAC) layer monitoring tool that aggregates traffic at intervals with seconds resolution. Measurements are performed in an operating LTE network. We use a deep learning method implementing a Long Short-Term Memory (LSTM) Recurrent Neural Network (RNN), to determine if the traffic pattern is the healthy one, or it is anomalous, with missing packets and jitter. We identify key metrics in the monitoring data, that are selected as features in the RNN, which enable the detection of fine time resolution traffic anomalies hidden in the aggregated and coarse measurements reported by the monitoring tool. We find that applying the proposed approach, a vertical is able to determine whether the communication over the wireless network is healthy or anomalous. Finally, we discuss on the use of the proposed monitoring approach in 4G networks, and learning possibilities for 5G standardization in terms of monitoring metrics, features, monitoring resolution, service concepts, etc.