Supervised and Semi-Supervised Learning for Failure Identification in Microwave Networks

Automated failure-cause identification in communication networks allows operators to reduce service unavailability. Once the most likely failure root-cause is identified, appropriate countermeasures can be effectively put in place (e.g., by choosing an in-field intervention vs. a remote equipment reconfiguration). In this article, we describe a successful application of Machine Learning (ML) for automatic failure identification in microwave networks based on the real-field data. On microwave links, different heterogeneous causes (e.g., adverse atmospheric conditions, or obstacles) lead to service unavailability and produce not easily-distinguishable degradation effects on the transmission parameters. Hence, failure identification is traditionally accomplished by domain experts via direct inspection of transmission-parameter logs. As a first contribution, we identify six categories of failure causes in microwave networks and show that supervised ML enables very accurate failure identification, hence significantly simplifying failure troubleshooting. Comparing various ML algorithms, we find that up to 93% classification accuracy is obtained using real-field labeled datasets with 2513 points. One main hindrance to the application of supervised learning is that, in real network deployments, limited amount of labeled data is available for training, as manual labeling is performed by domain experts based on their knowledge and experience. On the other hand, collecting unlabeled data is relatively simple as network management systems retrieve large amounts of unlabeled information automatically. As a second contribution, we investigate an automated labeling procedure, based on autoencoders-like Artificial Neural Networks, to combine the knowledge of the few manually-labeled data with large unlabeled data. Results show that our data augmentation based on autoencoders can slightly improve failure-cause identification only when Artificial Neural Networks or Support Vector Machines are used, while accuracy slightly decreases when adopting Random Forest.