Proactive microwave link anomaly detection in cellular data networks

Microwave links are widely used in cellular networks for large-scale data transmission. From the network operators’ perspective, it is critical to quickly and accurately detect microwave link failures before they actually happen, thereby maintaining the robustness of the data transmissions. We present PMADS, a machine-learning-based proactive microwave link anomaly detection system that exploits both performance data and network topological information to detect microwave link anomalies that may eventually lead to actual failures. Our key observation is that anomalous links often share similar network topological properties, thereby enabling us to further improve the detection accuracy. To this end, PMADS adopts a network-embedding-based approach to encode topological information into features. It further adopts a novel active learning algorithm, ADAL, to continuously update the detection model at low cost by first applying unsupervised learning to separate anomalies as outliers from the training set. We evaluate PMADS on a real-world dataset collected from 2142 microwave links in a production LTE network during a one-month period, and show that PMADS achieves a precision of 94.4% and a recall of 87.1%. Furthermore, using the active learning feedback loop, only 7% of the training data is required to achieve comparable results. PMADS is currently deployed in a metropolitan LTE network that serves around four million subscribers in a Middle Eastern country.