Machine Learning Models for Predictive Maintenance and Performance Optimization in Telecom Infrastructure

Abstract

Internet Protocol (IP) networks transform entire global communication systems. The growing demand for IP networks with immense capacity and ultra-low latency has substantially changed telecommunications operators’ infrastructures. As the number of elements within these infrastructures has multiplied, telcos must shift from today’s profitability-driven network maintenance to a proactive approach. Reactive maintenance using simple indicators and counter levels will lead to network failure, affecting customer experience and business continuity. Hence, there is a growing need for Predictive Maintenance. Telco predictive maintenance and performance optimization efforts focus on Machine Learning models that leverage historical data within data warehouses. Such databases comprise both structured and unstructured data related to network design, operation, and performance. The use of temp-data with new technologies represents a further breakthrough in regulatory telecom management. Different analysis and predictive analytics models have been developed, from basic statistical models to complex algorithms.
Imperceptibly, many solutions have been implemented and used successfully. However, a significant challenge associated with these models, such as extreme events or unplanned IP element maintenance, hampers their use. Relying solely on historical data creates inherent limitations, as data patterns will change or disappear risk being out of service. Despite model advances, telecom data storage and technology create additional predictive maintenance challenges. As new data generation levels rise, current data warehouses will become too expensive. Even with cost-efficient storage, questions arise regarding data relevance. Additionally, as network elements become homogenized, clearly defined settings lead to standard behavior. Moreover, storage issues exist due to network evolution over time, data system merge, and tele-operational sequence changes, where the absence of historical data creates knowledge gaps despite passing internal and third-party regulations.