🤖 AI Summary
This work addresses the challenge of abrupt temporal metric anomalies in large-scale base station testing, often caused by resource allocation errors, which necessitate efficient unsupervised detection methods. The authors propose CALM, a framework that leverages nonparametric kernel density estimation combined with bootstrap-based dynamic thresholding to enable real-time anomaly detection at the individual testbed level. To mitigate alert fatigue and identify globally significant anomalies, they further introduce AggCALM, which aggregates signals across multiple testbeds. Requiring no labeled data, the approach offers high timeliness and scalability, demonstrating strong performance on both simulated and real-world base station datasets. Beyond ensuring stable operation in complex testing environments, the method is readily transferable to other system health monitoring scenarios.
📝 Abstract
Large-scale testing infrastructures are critical for validating telecommunication systems, yet their growing complexity makes efficient resource utilization and anomaly detection increasingly challenging. In reservation-based testbed environments, errors in resource allocation or preparation often manifest as abrupt spikes or regime changes in time-based metrics. This paper proposes a scalable, unsupervised framework for real-time anomaly detection in such environments. We introduce CALM (Continuous Anomaly Localization for univariate and Multivariate data), a nonparametric method based on kernel density estimation and bootstrap-based thresholding, designed for anomaly detection at the individual testbed level. To address system-wide visibility, we further propose AggCALM, an aggregation framework that consolidates local anomaly signals across multiple testbeds to detect statistically significant global anomalies while mitigating alarm fatigue. The methodology is evaluated using simulated multivariate data and real-world data from a large-scale base station testing platform. Results demonstrate that the proposed framework enables timely, flexible, and accurate anomaly detection without requiring labeled data, supporting reliable operation of complex test environments. Although the proposed methodology is presented within the context of a telecommunication testing labs, it can be effectively used to other applications, such as condition monitoring, where anomaly detection serves as a pivotal pre-processing step for diagnostic signals.