Abstract
Localizing the root cause of network faults is crucial to network operation and maintenance (O&M). Significant operational expenses will be saved if the root cause can be identified agilely and accurately. However, this is challenging for human beings due to the complicated wireless environments and network architectures. Resorting to data analysis and machine learning is promising but remains difficult due to various practical issues, such as the lack of well-labeled samples, hybrid fault behaviors, missing data, and so on. In this paper, we introduce a novel real-world dataset for wireless communication network fault diagnosis. The goal is to infer the root cause timely when we observe certain symptoms in a network. Several baseline methods are provided.
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