JADE: Data-Driven Automated Jammer Detection Framework for Operational Mobile Networks

Wireless jammer activity from malicious or malfunctioning devices cause significant disruption to mobile network services and user QoE degradation. In practice, detection of such activity is manually intensive and costly, taking days and weeks after the jammer activation to detect it. We present a novel data-driven jammer detection framework termed JADE that leverages continually collected operator-side cell-level KPIs to automate this process. As part of this framework, we develop two deep learning based semi-supervised anomaly detection methods tailored for the jammer detection use case. JADE features further innovations, including an adaptive thresholding mechanism and transfer learning based training to efficiently scale JADE for operation in real-world mobile networks. Using a real-world 4G RAN dataset from a multinational mobile network operator, we demonstrate the efficacy of proposed jammer detection methods relative to commonly used anomaly detection methods. We also demonstrate the robustness of our proposed methods in accurately detecting jammer activity across multiple frequency bands and diverse types of jammers. We present real-world validation results from applying our methods in the operator's network for online jammer detection. We also present promising results on pinpointing jammer locations when our methods spot jammer activity in the network along with cell site location data.