Scaling Deep Learning Models for Spectrum Anomaly Detection
Zhijing Li
Zhujun Xiao
Bolun Wang
Ben Y. Zhao
Haitao Zheng
Proceedings of the 20th International Symposium on Mobile Ad Hoc Networking and Computing (MobiHoc 2019)
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Paper Abstract
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) models 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.