SpecEES: Scaling Deep Learning Models for Cellular Spectrum Monitoring

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Spectrum management in wireless networks is a challenging task that will only increase in difficulty as complexity grows in hardware, configurations, and new access technology. Government agencies and wireless providers need robust and flexible tools to monitor and detect anomalies (i.e., both faults and misbehavior) in physical spectrum usage, and to deploy them at scale. This project targets the open challenge of spectrum anomaly detection for wide-area cellular networks, providing a practical architecture to monitor, diagnose, and secure spectrum usage. The project will significantly improve spectrum efficiency, while tackling multiple open challenges related to energy efficiency and security. The technical component of the project will be tightly integrated with educational and outreach programs to engage female and underrepresented students into research, while offering a mentoring platform. The project will develop and strengthen collaborations with academic, government and industry partners, across multiple areas of spectrum measurements, energy-efficient systems, security, and applied machine learning.The core concept driving this project is the integration of a distributed spectrum measurement platform with an efficient deep neural network-based anomaly detection framework. Deep learning in this context introduces multiple challenges, including scalability, lack of location-specific training data, lack of misuse events in data, and potential for adversarial attacks. Our proposed work addresses these with a combination of context-agnostic training, transfer learning, semi-supervised clustering for detection of anomalies and adversarial countermeasures. The proposed system will incorporate spectrum measurements taken by various static and mobile observers, and use these measurements to train an efficient, scalable, and robust anomaly detection module driven by deep neural network models. The detection module will run on both static and mobile observers, allowing the system to detect and diagnose spectrum anomalies in real-time.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.