基于可扩展核密度估计框架的基站测试床实时异常检测

中文摘要 AI 辅助

大规模测试基础设施对电信系统验证至关重要，但其日益增长的复杂性使高效资源利用与异常检测愈发具有挑战性。在基于预留的测试床环境中，资源分配或准备错误常表现为时序指标的突发峰值或状态变化。本文提出一种适用于此类环境的可扩展无监督实时异常检测框架。我们引入CALM（单变量与多变量数据的连续异常定位），这是一种基于核密度估计与自助法阈值化的非参数方法，设计用于单个测试床级别的异常检测。为实现全系统可见性，我们进一步提出AggCALM，一种聚合框架，可整合多个测试床的局部异常信号，以检测具有统计显著性的全局异常，同时缓解警报疲劳。该方法通过模拟多变量数据与来自大规模基站测试平台的真实世界数据进行评估。结果表明，所提出的框架无需标记数据即可实现及时、灵活且准确的异常检测，支持复杂测试环境的可靠运行。尽管本文在电信测试实验室背景下呈现该方法，但其可有效用于其他应用，如状态监测，其中异常检测作为诊断信号的关键预处理步骤。

英文摘要

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.

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13 pages, 3 figures