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.

References

• Y. Ahn and J. P. Jeong, “An Intent-Driven Management Automation for 5G Mobile Networks,” in Proc. 2024 Int. Conf. Information Networking (ICOIN), Ho Chi Minh City, Vietnam, 2024, pp. 714–719, doi: 10.1109/ICOIN59985.2024.10572068.
• S. Wang, M. Ruiz, and L. Velasco, “Context-Based e2e Autonomous Operation in B5G Networks,” Sensors, vol. 24, no. 5, p. 1625, 2024, doi: 10.3390/s24051625.
• L. M. P. Larsen, A. Checko, and H. L. Christiansen, “A Survey of the Functional Splits Proposed for 5G Mobile Crosshaul Networks,” IEEE Commun. Surveys Tuts., vol. 21, no. 1, pp. 146–172, 1st Quart., 2019, doi: 10.1109/COMST.2018.2868805.
• A. Shahraki, M. Abbasi, M. J. Piran, and A. Taherkordi, “A Comprehensive Survey on 6G Networks: Applications, Core Services, Enabling Technologies, and Future Challenges,” arXiv:2101.12475, 2021.
• W. Jiang, B. Han, M. A. Habibi, and H. D. Schotten, “The Road Towards 6G: A Comprehensive Survey,” IEEE Open J. Commun. Soc., vol. 2, pp. 334–366, 2021, doi: 10.1109/OJCOMS.2021.3057679.
• N. Koursioumpas, S. Barmpounakis, I. Stavrakakis, and N. Alonistioti, “AI-Driven, Context-Aware Profiling for 5G and Beyond Networks,” IEEE Trans. Netw. Service Manag., vol. 19, no. 2, pp. 1036–1048, 2022, doi: 10.1109/TNSM.2021.3126948.
• L. Velasco, P. González, and M. Ruiz, “Distributed Intelligence for Pervasive Optical Network Telemetry,” J. Opt. Commun. Netw., vol. 15, no. 9, pp. 676–686, 2023, doi: 10.1364/JOCN.493347.
• M. Ruiz, F. Tabatabaeimehr, and L. Velasco, “Knowledge Management in Optical Networks: Architecture, Methods and Use Cases,” J. Opt. Commun. Netw., vol. 12, no. 10, pp. A70–A81, 2020, doi: 10.1364/JOCN.401051.
• M. Sulaiman, A. Moayyedi, M. A. Salahuddin, R. Boutaba, and A. Saleh, “Multi-Agent Deep Reinforcement Learning for Slicing and Admission Control in 5G C-RAN,” in Proc. 2022 IEEE/IFIP Network Operations and Management Symp. (NOMS), 2022, pp. 1–9, doi: 10.1109/NOMS54207.2022.9789880.
• A. Gavras et al., “5G PPP Architecture Working Group: View on 5G Architecture,” Version 4.0, European Commission, 2021.
• European Telecommunications Standards Institute, “Fixed 5th Generation Advanced and Beyond,” ETSI White Paper No. 48, 2022.
• M. Uusitalo et al., “6G Vision, Value, Use Cases and Technologies from European 6G Flagship Project Hexa-X,” IEEE Access, vol. 9, pp. 160004–160020, 2021, doi: 10.1109/ACCESS.2021.3130030.
• A. Bernal, M. Richart, M. Ruiz, A. Castro, and L. Velasco, “Near Real-Time Estimation of End-to-End Performance in Converged Fixed-Mobile Networks,” Comput. Commun., vol. 150, pp. 393–404, 2020, doi: 10.1016/j.comcom.2019.11.032.
• M. Ruiz, J. A. Hernández, M. Quagliotti, E. Hugues-Salas, E. Riccardi, A. Rafel, L. Velasco, and O. G. de Dios, “Network Traffic Analysis under Emerging Beyond-5G Scenarios for Multi-Band Optical Technology Adoption,” J. Opt. Commun. Netw., vol. 15, no. 2, pp. F36–F47, 2023, doi: 10.1364/JOCN.478653.
• D. Larrabeiti, L. M. Contreras, G. Otero, J. A. Hernández, and J. P. F. Palacios, “Toward End-to-End Latency Management of 5G Network Slicing and Fronthaul Traffic,” Opt. Fiber Technol., vol. 76, p. 103220, 2023, doi: 10.1016/j.yofte.2023.103220.
• A. Diez-Olivan, J. Del Ser, D. Galar, and B. Sierra, “Data Fusion and Machine Learning for Industrial Prognosis: Trends and Perspectives Towards Industry 4.0,” Inf. Fusion, vol. 50, pp. 92–111, 2019, doi: 10.1016/j.inffus.2018.10.005.
• D. Jiang, Y. Wang, Z. Lv, S. Qi, and S. Singh, “Big Data Analysis Based Network Behavior Insight of Cellular Networks for Industry 4.0 Applications,” IEEE Trans. Ind. Informat., vol. 16, no. 2, pp. 1310–1320, 2020, doi: 10.1109/TII.2019.2930516.
• P. Magdalinos, “A Context Extraction and Profiling Engine for 5G Network Resource Mapping,” Comput. Commun., vol. 109, pp. 184–201, 2017, doi: 10.1016/j.comcom.2017.06.009.
• L. Le, D. Sinh, B. P. Lin, and L. Tung, “Applying Big Data, Machine Learning, and SDN/NFV to 5G Traffic Clustering, Forecasting, and Management,” in Proc. 2018 4th IEEE Conf. Network Softwarization Workshops (NetSoft Workshops), 2018, pp. 168–176, doi: 10.1109/NETSOFT.2018.8460098.
• U. Karneyenka, K. Mohta, and M. Moh, “Location and Mobility Aware Resource Management for 5G Cloud Radio Access Networks,” in Proc. 2017 Int. Conf. High Performance Computing & Simulation (HPCS), 2017, pp. 168–175, doi: 10.1109/HPCS.2017.29.
• M. S. Parwez, D. B. Rawat, and M. Garuba, “Big Data Analytics for User-Activity Analysis and User-Anomaly Detection in Mobile Wireless Network,” IEEE Trans. Ind. Informat., vol. 13, no. 4, pp. 2058–2065, 2017, doi: 10.1109/TII.2017.2700327.
• S. Barmpounakis, A. Kaloxylos, P. Spapis, C. Zhou, P. Magdalinos, and N. Alonistioti, “Data Analytics for 5G Networks: A Complete Framework for Network Access Selection and Traffic Steering,” Int. J. Adv. Telecommun., vol. 11, no. 3&4, pp. 101–114, 2018.
• L. Xu and R. Duan, “Towards Smart Networking through Context Aware Traffic Identification Kit (TrICK) in 5G,” in Proc. 2018 Int. Symp. Networks, Computers and Communications (ISNCC), 2018, pp. 1–6, doi: 10.1109/ISNCC.2018.85309