The massive amount of data available in operational mobile networks offers an invaluable opportunity for operators to detect and analyze possible anomalies and predict network performance. In particular, application of advanced machine learning (ML) techniques on data aggregated from multiple sources can lead to important insights, not only for the detection of anomalous behavior but also for performance forecasting, thereby complementing classic network operation and maintenance solutions with intelligent monitoring tools. In this paper, we propose a novel framework that aggregates diverse data sets (e.g. configuration, performance, inventory, locations, user speeds) from an operational LTE network and applies ML algorithms to diagnose network issues and analyze their impact on key performance indicators. To this end, pattern identification and time-series forecasting algorithms are used on the ingested data. Results show that proposed framework can indeed be leveraged to automate the identification of anomalous behaviors associated with the spatial-temporal characteristics, and predict customer impact in an accurate manner.
翻译:运行中的移动网络提供的大量数据为操作者提供了一个宝贵的机会,以发现和分析可能的异常现象并预测网络性能,特别是应用从多种来源收集的数据的先进机器学习技术,可以导致重要的洞察力,不仅用于检测异常行为,而且用于绩效预测,从而用智能监测工具补充典型的网络操作和维护解决方案。在本文件中,我们提出了一个新框架,将运行中的LTE网络的不同数据集(例如配置、性能、库存、地点、用户速度)汇总起来,并应用ML算法来诊断网络问题,分析其对关键业绩指标的影响。为此,在输入的数据中使用了模式识别和时间序列预测算法。结果显示,拟议的框架确实可以用来自动识别与空间-时空特征相关的异常行为,并准确预测客户影响。