AI-Enabled Fault Prediction and Performance Optimization in 5G Optical Transport Networks

Abstract

The rapid deployment of fifth-generation mobile networks (5G) is transforming the optical transport from a static capacity layer into a dynamic, service-critical infrastructure that must support cloud radio access, edge computing, network slicing, industrial connectivity and latency-sensitive consumer services. Faults in this layer may degrade fronthaul, midhaul and backhaul performance before a hard alarm appears. Over-conservative engineering margins contribute to cost, energy consumption and wasted spectrum. This paper discusses the potential of artificial intelligence to enable fault prediction and performance optimization in 5G optical transport networks through the integration of optical performance monitoring, telemetry analytics, digital twins, machine learning, software-defined control and closed-loop assurance. We adopt a structured narrative review approach to synthesise the literature published from 2020 to 2025 on optical failure management, quality-of-transmission prediction, loss-of-signal forecasting, soft-failure localisation, traffic forecasting, transport automation, and 5G optical fronthaul design. This paper proposes a practical AI-enabled operating framework to convert streaming measurements to failure probability, time-to-impact, root-cause ranking, optimization recommendations and governed automation actions. The review suggests that the greatest value of AI is to embed models into operational processes, rather than to use them as standalone prediction tools. For telecom operators, the key benefits are reduced mean time to repair, fewer preventable outages, improved spectrum and power efficiency, better service-level assurance and more disciplined capacity expansion. The study concludes with a phased implementation roadmap for operators seeking to move away from reactive maintenance and towards predictive, explainable and policy-controlled optical transport operations.