ML-Based Fault Management Automation in Large-Scale Fixed and Mobile Telecommunication Networks

Many network faults are flooding the telecommunication companies in the form of Trouble Tickets (TT). Automation in managing these TTs is vital in increasing customer satisfaction. We develop a solution to address two challenges regarding TTs generated from fixed and mobile access network domains: prediction of resolution times and technician dispatch needs. Our study utilizes datasets from Telenor, a Swedish telecommunication operator, encompassing 35,000 access switches and 8,000 base stations. It incorporates 40,000 switch TTs and 22,000 mobile TTs during 2019-2023. None of the previous works studied multiple telecommunication domains or considered the time evolution of TTs. This work comprehensively studies several prediction models for the mentioned use cases and network domains. Our models successfully outperform the company baseline and best proposed state-of-the-art models. Within 1-hour confidence interval, our method can correctly predict shortest ranges of resolution times for 90% of switch TTs and 80% of mobile TTs. We also predict the necessity of dispatching workforce to the place with weighted F1 scores of respectively, 88% and 89% for switch and mobile TTs which shows high average accuracy of our system in prediction across both dispatch and non-dispatch TT classes to assist operation. With these scores, our model is capable of allocating resources automatically, enhancing customer satisfaction. We also studied the TTs evolution, for example, for switch TTs, within 15 minutes of creation time, prediction improves by 57% and 50%, for resolution and dispatch prediction, respectively.