Scaling Deep Learning Models for Spectrum Anomaly Detection

Spectrum management in cellular networks is a challenging task that will only increase in difficulty as complexity grows in hardware, configurations, and new access technology (e.g. LTE for IoT devices). Wireless providers need robust and flexible tools to monitor and detect faults and misbehavior in physical spectrum usage, and to deploy them at scale. In this paper, we explore the design of such a system by building deep neural network (DNN) models1 to capture spectrum usage patterns and use them as baselines to detect spectrum usage anomalies resulting from faults and misuse. Using detailed LTE spectrum measurements, we show that the key challenge facing this design is model scalability, i.e. how to train and deploy DNN models at a large number of static and mobile observers located throughout the network. We address this challenge by building context-agnostic models for spectrum usage and applying transfer learning to minimize training time and dataset constraints. The end result is a practical DNN model that can be easily deployed on both mobile and static observers, enabling timely detection of spectrum anomalies across LTE networks.