Agentic AI-Driven Self-Healing Communication Networks for Autonomous 6G Service Restoration
Keywords:
Agentic AI, 6G Networks, Self-Healing Networks, Autonomous Network Management, Service Restoration, Artificial Intelligence, Reinforcement Learning, Network Slicing, Open RAN, Anomaly Detection, Root-Cause Analysis, Closed-Loop Automation, Network Resilience, Network Digital Twin, Zero-Touch NetworkingAbstract
The increasing heterogeneity, virtualization, and service diversity of sixth-generation (6G) communication networks require autonomous mechanisms capable of detecting failures, identifying their causes, and restoring affected services with minimal human intervention. Conventional network management approaches generally depend on predefined rules and human-operated recovery procedures, which may be inadequate for highly dynamic and distributed 6G environments. This paper proposes an Agentic AI-Driven Self-Healing Communication Network framework for autonomous 6G service restoration. The proposed architecture integrates continuous network telemetry, artificial intelligence-based anomaly detection, root-cause analysis, agentic decision-making, and closed-loop service restoration within a unified control framework. An intelligent agent observes network states and service-level indicators, evaluates detected faults, selects appropriate recovery actions, and coordinates network reconfiguration through programmable control interfaces. Reinforcement learning is considered for adaptive restoration-policy selection, while predictive analytics supports proactive fault identification. The framework can incorporate network slicing, Open RAN, edge computing, and network digital twins to improve restoration flexibility and reduce service disruption. A simulation-based evaluation is proposed using service availability, fault detection accuracy, restoration time, packet loss, latency, throughput, resource utilization, and successful recovery rate as key performance indicators. The proposed approach aims to demonstrate how agentic intelligence can transform conventional reactive network management into an autonomous observe–reason–act–verify–learn cycle for resilient 6G service delivery.
References
[1]. W. Saad, M. Bennis, and M. Chen, “A Vision of 6G Wireless Systems: Applications, Trends, Technologies, and Open Research Problems,” IEEE Network, vol. 34, no. 3, pp. 134–142, May/Jun. 2020, doi: 10.1109/MNET.001.1900287.
[2]. H. Tataria, M. Shafi, A. F. Molisch, M. Dohler, H. Sjöland, and F. Tufvesson, “6G Wireless Systems: Vision, Requirements, Challenges, Insights, and Opportunities,” Proceedings of the IEEE, vol. 109, no. 7, pp. 1166–1199, 2021.
[3]. X. You, C.-X. Wang, J. Huang, et al., “Towards 6G Wireless Communication Networks: Vision, Enabling Technologies, and New Paradigm Shifts,” Science China Information Sciences, vol. 64, no. 1, 110301, 2021, doi: 10.1007/s11432-020-2955-6.
[4]. C. Zhang, P. Patras, and H. Haddadi, “Deep Learning in Mobile and Wireless Networking: A Survey,” IEEE Communications Surveys & Tutorials, vol. 21, no. 3, pp. 2224–2287, 2019, doi: 10.1109/COMST.2019.2904897.
[5]. J. Moysen and L. Giupponi, “From 4G to 5G: Self-Organized Network Management Meets Machine Learning,” Computer Communications, vol. 129, pp. 248–268, 2018, doi: 10.1016/j.comcom.2018.07.015.
[6]. H. Fourati, R. Maaloul, L. Chaari, and M. Jmaiel, “Comprehensive Survey on Self-Organizing Cellular Network Approaches Applied to 5G Networks,” Computer Networks, vol. 199, 108435, 2021, doi: 10.1016/j.comnet.2021.108435.
[7]. M. El Rajab, L. Yang, and A. Shami, “Zero-Touch Networks: Towards Next-Generation Network Automation,” Computer Networks, vol. 243, 110294, 2024, doi: 10.1016/j.comnet.2024.110294.
[8]. A. A. de Souza, et al., “Zero Touch Management: A Survey of Network Automation Solutions for 5G and 6G Networks,” IEEE Communications Surveys & Tutorials, vol. 24, no. 4, pp. 2535–2578, 2022, doi: 10.1109/COMST.2022.3212586.
[9]. R. Boutaba, N. Shahriar, M. A. Salahuddin, S. R. Chowdhury, N. Saha, and A. James, “AI-Driven Closed-Loop Automation in 5G and Beyond Mobile Networks,” in Proc. ACM Workshop on Flexible Networks Artificial Intelligence Supported, 2021, pp. 1–6, doi: 10.1145/3472735.3474458.
[10]. S. K. Nalluri, V. T. Bathini and V. K. B. Parasaram, "AI-Augmented Manufacturing Execution Systems for Precision Healthcare: A Scalable Framework for Smart Genomic and Proteomic Production," 2026 IEEE 2nd International Conference on Secure IoT, Assured and Trusted Computing (SATC), Houston, TX, USA, 2026, pp. 1-6, doi: 10.1109/SATC69565.2026.11542384.
[11]. X. Foukas, G. Patounas, A. Elmokashfi, and M. K. Marina, “Network Slicing in 5G: Survey and Challenges,” IEEE Communications Magazine, vol. 55, no. 5, pp. 94–100, 2017, doi: 10.1109/MCOM.2017.1600951.
[12]. O. M. Caicedo Rendon, J. A. Hurtado Salazar, and K. C. Silasima, “Deep Reinforcement Learning for Resource Management on Network Slicing: A Survey,” Sensors, vol. 22, no. 8, 3031, 2022.
[13]. Y. Xie, Y. Kong, L. Huang, S. Wang, S. Xu, X. Wang, and J. Ren, “Resource Allocation for Network Slicing in Dynamic Multi-Tenant Networks: A Deep Reinforcement Learning Approach,” Computer Communications, vol. 195, pp. 476–487, 2022, doi: 10.1016/j.comcom.2022.09.015.
[14]. D. Xiao, S. Chen, W. Ni, J. Zhang, A. Zhang, and R. Liu, “A Sub-Action Aided Deep Reinforcement Learning Framework for Latency-Sensitive Network Slicing,” Computer Networks, vol. 217, 109279, 2022, doi: 10.1016/j.comnet.2022.109279.
[15]. “Data-Driven Dynamic Resource Scheduling for Network Slicing: A Deep Reinforcement Learning Approach,” Information Sciences, 2019.
[16]. Vishram Singh, “Real-Time Financial Data Validation Platform for Enterprise Accounting Systems”, IJCISIM, vol. 18, no. 10s, pp. 467–481, Jul. 2026.
[17]. J. Jagannath, N. Polosky, A. Jagannath, F. Restuccia, and T. Melodia, “Machine Learning for Wireless Communications in the Internet of Things: A Comprehensive Survey,” Ad Hoc Networks, vol. 93, 101913, 2019, doi: 10.1016/j.adhoc.2019.101913.
[18]. C. Koneni and S. Lokula, "Evidence-Quality Telemetry for Cloud Incident Response: Detecting Gaps, Drift, and Integrity Failures in Large-Scale Observability Pipelines," 2026 14th International Symposium on Digital Forensics and Security (ISDFS), Boston, MA, USA, 2026, pp. 1-6, doi: 10.1109/ISDFS69419.2026.11459097.
[19]. Z. Li, et al., “A Survey of Network Automation for Industrial Internet-of-Things Toward Industry 5.0,” IEEE Transactions on Industrial Informatics, vol. 19, no. 2, pp. 2065–2077, 2023, doi: 10.1109/TII.2022.3215231.
[20]. “Machine Learning Applied to Anomaly Detection on 5G O-RAN Architecture,” Procedia Computer Science, vol. 222, pp. 81–93, 2023, doi: 10.1016/j.procs.2023.08.146.
[21]. E. Habler, R. Bitton, D. Avraham, D. Mimran, E. Klevansky, O. Brodt, H. Lehmann, Y. Elovici, and A. Shabtai, “Adversarial Machine Learning Threat Analysis and Remediation in Open Radio Access Network (O-RAN),” Journal of Network and Computer Applications, vol. 236, 104090, 2025, doi: 10.1016/j.jnca.2024.104090.
[22]. “An Open Testbed for O-RAN Experimentation with AI-Enabled Control and Monitoring,” Internet of Things, vol. 33, 101729, 2025, doi: 10.1016/j.iot.2025.101729.
[23]. Vishram Singh, 2026. "Enterprise Resource Planning for Financial Applications: Engineering, Architecture, and Design Principles," International Journal of Scientific Research in Science and Technology, Technoscience Academy, vol. 13(2), pages 653-661, April.
[24]. W. Wu, X. Huang, and T. H. Luan, “AI-Native Network Digital Twin for Intelligent Network Management in 6G,” 2024.
[25]. X. Wang, L. Lu, Q. Li, Q. Sun, N. Shi, Z. Chen, and T. Sun, “A Task-Driven Design Approach for 6G AI-Native Architecture,” Engineering, vol. 56, pp. 87–103, 2026, doi: 10.1016/j.eng.2025.09.005.
[26]. “Enabling Mobile AI Agent in 6G Era: Architecture and Key Technologies,” IEEE Network, vol. 38, no. 5, pp. 66–75, 2024, doi: 10.1109/MNET.2024.3422309.
[27]. C. Chatzieleftheriou and E. Liotou, “A Survey on AI for 6G: Challenges and Opportunities,” IEEE Open Journal of the Communications Society, vol. 7, pp. 3189–3221, 2026, doi: 10.1109/OJCOMS.2026.3677293.
[28]. M. Ferrag, et al., “6G Needs Agents: Toward Agentic AI-Native Networks for Autonomous Intelligence,” IEEE Open Journal of the Communications Society, vol. 7, pp. 7254–7282, 2026, doi: 10.1109/OJCOMS.2026.3707904.
[29]. P. Kamarajapandian, S. Nithya, A. Syed Haroon, V. Bharathi, A. Kaliappan, and L. Meenachi, “Intent-Based Networking Using LLM-Driven Agentic Reinforcement Learning for Zero-Touch 5G/6G and IoT Systems,” Computer Communications, vol. 257, 108602, 2026, doi: 10.1016/j.comcom.2026.108602.
[30]. Koneni, C., S. Lokula, and L. Panjiyar. "A Cloud-Native Approach to Statistical Anomaly Detection and Automated Data Quality Validation." International Journal of Data Science and IoT Management System 4.4 (2025): 533-543
[31]. V. K. Bharadwaj Parasaram, V. Teja Bathini and S. K. Nalluri, "An Intelligent Digital Framework for Clinical Trial Monitoring Using AI and Cloud-Based Software Platforms," 2026 2nd International Conference on Artificial Intelligence Systems (AIS), San Antonio, TX, USA, 2026, pp. 524-530, doi: 10.1109/AIS69919.2026.11621841.
