Spatio-Temporal Analysis and Prediction of Mass Telecommunication Base Station Failure Events

Abstract

Large-scale telecommunication systems are lifeline infrastructure in modern society. A telecommunication system typically consists of a huge number of base stations with diverse geographical locations across a country, which highly complicates maintenance operations. To allocate maintenance resource properly, it is important to have a good understanding on the failure pattern of these base stations. Statistical inference of recurrent failures of these base stations is challenging because of the large number of base stations and the spatial correlation of their failure processes. Based on eight-month failure data of telecommunication base stations in Harbin, China, we propose a customized nonhomogeneous Poisson process (NHPP) model for recurrent failure data from telecommunication systems. The model consists of two layers, where the temporal layer applies an NHPP with station-specific frailty for failures of each base station, and the spatial layer uses a multivariate lognormal distribution to characterize the correlation among the frailties. The Monte Carlo EM (MCEM) algorithm is applied to estimate parameters included in the proposed model. We demonstrate the proposed model using the Harbin telecommunication system example with 7725 base stations and 4615 failure records.

Keywords:

• Failure prediction
• MCEM algorithm
• NHPP
• Spatial frailty
• Telecommunication system

Supplementary Materials

The following supplementary materials are available online.

Additional details: Additional numerical details and results in simulation and case studies, and details of the Bayesian MCMC method.

Data: A sample dataset of the Harbin telecommunication base station failure data is provided for reference. The full dataset is available from the authors upon reasonable request and with permission of China Tower Corporation, Heilongjiang Branch.

Code: Python codes for simulation and case studies.

Acknowledgments

The authors would like to thank the editor, the associate editor, and two anonymous reviewers for their constructive comments which have led to a substantial improvement to the initial version of the article.

Disclosure Statement

The authors report there are no competing interests to declare.

Additional information

Funding

This research is an outcome of MOE Tier 2 grant R-266-000-143-112, and the Future Resilient Systems project supported by the National Research Foundation, Prime Minister’s Office, Singapore under its Campus for Research Excellence and Technological Enterprise (CREATE) program.