AI-Driven Autonomous Telecom Operations for Advanced Carrier Networks

Abstract

The rapid evolution of 5G-Advanced and emerging 6G networks is driving telecom operators toward highly autonomous, intelligent, and self-optimizing operational models. AI-driven autonomous telecom operations integrate artificial intelligence, machine learning, deep learning, reinforcement learning, and automation to transform conventional network management into proactive, closed-loop operations. These technologies enable carrier networks to continuously monitor performance, detect anomalies, predict failures, optimize resources, and automatically implement corrective actions with minimal human intervention. AI-based analytics can correlate large volumes of data generated by network elements, cloud platforms, edge infrastructure, and customer services to improve fault management, traffic engineering, energy efficiency, and quality of experience. Digital twins and intent-based networking further support predictive simulation and policy-driven network optimization. The integration of generative AI and autonomous agents can enhance operational decision-making, simplify network configuration, and accelerate service assurance. However, achieving fully autonomous operations requires addressing challenges related to data quality, explainability, cybersecurity, interoperability, model reliability, regulatory compliance, and human oversight. This paper examines the architecture, technologies, operational benefits, and challenges of AI-driven autonomous telecom operations for advanced carrier networks. It also discusses future directions toward self-healing, self-optimizing, and self-evolving networks capable of dynamically adapting to increasingly complex and heterogeneous communication environments