Hardware maintenance is one of the key issues faced by Communications Service Providers. CSP networks require continuous monitoring to ensure that the number of failures is kept to a minimum and to provide constant Quality of Service (QoS) for subscribers.

In addition to avoiding service failures, CSPs have to ensure that their infrastructure provides enough capacity for their subscribers. As data usage is on the increase, the currently existing network infrastructure such as base stations may not be able to cope with increasing traffic.

Therefore, learning about any forthcoming faults as early as possible and maintaining sufficient infrastructure capacity before any deficiencies are felt by the subscribers are two crucial issues for CSPs. Elitnet’s Predictive Network Maintenance solution addresses both of these matters by collecting data from numerous network sources and detecting anomalies using Artificial Intelligence (AI)/Machine Learning (ML) methods.

Using the Predictive Network Maintenance solution, the CSP can use the received information to repair forthcoming faults in various network components and use the provided increased capacity requirement forecasts to plan the necessary infrastructure expansions.

The solution is implemented as a collection of data processors/applications for Elitnet’s Data Analytics Platform, a high-performance data analysis platform which covers data collection from multiple sources, AI/ML enabled data processing, and powerful reporting and monitoring tools.

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