Paper Info
Title
Flexible O&M for Telecom Networks at Huawei: A Language Model-based Approach
Abstract
ABSTRACTFlexible operation and maintenance (O&M) is critical for telecommunication (telecom) service providers due to the ever-growing communication networks. Currently, most O&M operations still rely on rule-based strategies, which only cover limited scenarios and are costly to extend for novel applications as expert knowledge is intensively involved. To build a more flexible O&M system, we propose a language model to extract useful representations out of massive network signaling messages and use the representations to perform downstream O&M tasks. Given that a vanilla language model is not directly applicable for the structured signaling messages, we develop an expert-knowledge-inspired statistical approach to preprocess the messages and a hierarchical network architecture to extract message relations among different levels. Moreover, network messages in the real world are often contaminated, which can mislead the language model to learn incorrect message patterns. To mitigate data contamination, we propose a reverse training method that prevents the language model from learning the contaminated data. We collected hundreds of thousands of signaling message flows to train the proposed signaling language model and applied the trained model to O&M tasks. Offline experiments show that our proposed language model captures various signaling protocols and the extracted representations enable us to achieve expert-level performance in network anomaly detection and service recognition. Our language model has been deployed online at Huawei and significantly improved O&M efficiency.
Year
DOI
Venue
2021
10.1145/3459637.3481919
Conference on Information and Knowledge Management
DocType
Citations 
PageRank 
Conference
0
0.34
References 
Authors
0
7
Name
Order
Citations
PageRank
Shuang Wu111216.94
Zhen Qin200.68
Zhen Wang34515.47
Siwei Rao400.68
Qiang Ye500.34
Xingyue Quan600.34
Guangjian Tian7144.56