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A Framework for Closed-Loop Automation in Autonomous Network Operations Centers: Reducing Mean-Time-to-Resolution in U.S. 5G Network Deployments: Closed-Loop Automation for Autonomous NOCs in 5G Networks
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 187 - Number 138 |
| Year of Publication: 2026 |
| Authors: Anjali Kalra |
10.5120/ijca78086862fd98
|
Anjali Kalra . A Framework for Closed-Loop Automation in Autonomous Network Operations Centers: Reducing Mean-Time-to-Resolution in U.S. 5G Network Deployments: Closed-Loop Automation for Autonomous NOCs in 5G Networks. International Journal of Computer Applications. 187, 138 ( Aug 2026), 58-66. DOI=10.5120/ijca78086862fd98
Abstract
As United States carriers densify fifth-generation (5G) radio access and transport infrastructure, Network Operations Centers (NOCs) face a sharp rise in alarm volume, event correlation complexity, and cross-domain fault interdependency that erodes Mean-Time-to-Resolution (MTTR) for service-impacting incidents. This paper proposes a five-layer closed-loop automation framework telemetry and data fusion, AI/ML-driven diagnosis, intent-driven decision and orchestration, automated remediation, and persistent knowledge management designed to compress the detect-diagnose-decide-act-verify incident lifecycle that dominates MTTR in autonomous NOC operations. The framework synthesizes intent-driven management automation principles, context-aware autonomous operation models, distributed telemetry and knowledge-management architectures, and machine-learning-based anomaly detection and traffic analytics drawn from the contemporary 5G/B5G literature. We map the framework against established network-autonomy maturity levels, discuss radio-access functional-split implications for fault-detection latency budgets, and present an illustrative MTTR stage-decomposition model contrasting manual and automated operation. The analysis indicates that the largest MTTR reduction opportunity lies in compressing the diagnose and decide stages through AI-assisted root-cause analysis and intent translation, rather than in the act stage alone. We conclude with open challenges cross-domain knowledge federation, model trust and explainability, and the transition path toward 6G-ready autonomous operations and outline a research agenda for closing the loop end-to-end in production 5G NOCs.
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