University of Waterloo

Published purpose

Canadian rural and remote communities often suffer from troublesome broadband Internet access and services resulting from irresponsive network operations and maintenance. With today’s artificial intelligence (AI) and machine learning (ML) technologies, we will adopt a data-driven approach to an automated diagnosis and troubleshooting process of network operational issues. Furthermore, with the use of multiple radio access technologies, an adaptive and intelligent decision can be made locally and collaboratively to provide responsive and uninterrupted self-maintainable solutions. This project aims to provide AI-assisted solutions to automate the network maintenance for operations and mitigate unnecessary site visits and wasted operations efforts. We will study the causal network models for root cause analysis of common access network issues, targeting at effective diagnostic solutions leading to autonomous and self-maintainable network operations for all residents living in rural and remote communities in Canada.