Self-Healing Telecom Networks with AI-Driven Autonomous Operations (AIOps)

Abstract

The current telecommunications infrastructure faces unparalleled operational complexity with the increasing proliferation of fifth-generation wireless systems, software-defined architectures, and distributed edge computing deployments over global networks. Traditional manual approaches to network management are insufficient to address issues of scale, velocity, and complexity in modern communication systems, where cascading failures lead to rapid service degradation impacting millions of subscribers. This article attempts to analyze the enabling approaches of Artificial Intelligence (AI)-driven autonomous operations of self-healing telecommunication networks that are capable of detecting anomalies, diagnosing root causes, and executing corrective actions without human involvement. This article discusses machine learning techniques for fault detection and prediction, autonomous remediation frameworks using reinforcement learning, and intent-based networking; intelligent algorithms for resource allocation and traffic engineering; and critical implementation issues regarding data quality, model reliability, and security vulnerabilities. Emerging technologies such as edge intelligence architectures, deep reinforcement learning, and distributed computing frameworks for intelligent networks present future directions that may offer enhanced functionality to overcome limitations. AI-powered self-healing networks fundamentally change telecommunications operations from the current reactive maintenance paradigms to proactive and predictive management solutions to optimally manage service quality, operational efficiency, and infrastructure utilization for increasingly complex network environments.