A Review of AI-Driven Predictive Maintenance in Telecommunications

Abstract

The telecommunications industry is rapidly evolving, driven by the increasing reliance on artificial intelligence (AI) to enhance network reliability and efficiency. Predictive maintenance (PdM) powered by AI has emerged as a crucial strategy for minimizing unexpected downtimes and optimizing service quality. Traditional reactive maintenance approaches often lead to inefficiencies, operational delays, and increased costs. This paper provides a comprehensive review of AI-driven predictive maintenance in telecommunications, categorizing existing research based on AI methodologies, applications, and real-world implementations. We analyze machine learning (ML), deep learning (DL), and explainable AI (XAI) techniques in fault detection, resource allocation, and performance optimization. A comparative analysis highlights the advantages and challenges of AI adoption, emphasizing key research gaps in scalability, ethical considerations, and integration with emerging technologies such as 5G, edge computing, and the Internet of Things (IoT). This study concludes by outlining future research directions and advocating for responsible AI deployment to ensure transparency, trust, and long-term sustainability in AI-driven predictive maintenance.