Adaptive Two-Stage Online Learning for Service-Affecting Failure Detection in Mobile Core Networks

AI 中文解读

手机网络出故障，运营商往往后知后觉——等用户投诉了才去修。现在有了新招：一个“两阶段在线学习”框架，让网络自己能实时“嗅出”故障信号。其核心技术是分两步走：第一步先掌握正常的流量规律，像个老司机熟悉路况；第二步一旦发现流量异常，立刻对照上下文信号判断是“真故障”还是“小波动”。这种方法完全在线运行，计算量小，能持续自我调整，精准度远超传统固定阈值监测。对于普通用户来说，这意味着手机信号更稳了——掉线、卡顿被提前发现和处理，你甚至在刷视频时都感觉不到网络在“自救”。运营商也能少接投诉电话，省下大把人力去排查维修。简单说，就是让移动网络的“免疫系统”自己学会了识别和报警，生活里的断网焦虑有望大大减少。

arXiv:2607.18522v1 Announce Type: new Abstract: Mobile network operators monitor aggregated traffic volumes to assess the operational health of core network infrastructure. Reliable failure detection is challenging due to strong temporal structure, non-stationarity, measurement artefacts, and extreme class imbalance, which limit static threshold-based monitoring. This paper proposes a two-stage online learning framework for traffic-based failure detection in mobile core networks. Stage I incrementally models normal traffic dynamics using lightweight regression with time-aware features. Stage II analyses prediction residuals together with contextual indicators to detect genuine service-affecting network failures. The framework operates fully online under a prequential evaluation protocol, enabling continuous adaptation with low computational overhead. Across linear and non-linear models, the proposed two-stage architecture achieves the best precision-recall trade-off, attaining the highest recall, F1-score, and AUC at acceptable false positive rates. These results demonstrate the importance of explicit residual decomposition for reliable failure detection in streaming mobile core network data.