AI-Driven Root Cause Analysis for Fixed Telecommunications Networks

Abstract

Root Cause Analysis (RCA) is a critical component of service assurance in fixed telecommunications networks, where service degradation may originate from multiple interconnected domains, including fixed access infrastructure, Optical Line Terminals (OLTs), Optical Network Terminals (ONTs), IP transport, uplinks, network capacity, Customer Premises Equipment (CPE), and home Wi-Fi environments. Traditional RCA approaches depend largely on predefined thresholds, alarm rules, individual monitoring systems, and manual correlation by network engineers. Although these methods remain effective for known and clearly defined faults, they become less efficient when network problems involve large volumes of operational data, intermittent degradation, multiple simultaneous symptoms, or dependencies across different network domains. This paper examines the application of Artificial Intelligence (AI) to enhance RCA in fixed telecommunications networks. It proposes a practical AIdriven RCA framework that combines network and service data, KPI and alarm monitoring, anomaly detection, multi-source data correlation, AI-based analysis, probable root-cause identification, corrective-action recommendations, and post-action performance validation. The proposed approach aims to transform RCA from a predominantly reactive and manually intensive process into a more intelligent and proactive service-assurance capability. Rather than replacing network engineers, AI-driven RCA is positioned as a decision-support mechanism that accelerates investigation, improves correlation across heterogeneous data sources, and enables more consistent identification of probable causes. The paper concludes that AI-driven RCA represents an important step toward increasingly intelligent and automated fixed telecommunications network operations