AI-DRIVEN AUTONOMOUS OPERATIONS FOR MVNO NETWORKS
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Sr. Solutions Architect & Engineering, MVNO Connect LLC, USA.
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Abstract
Mobile Virtual Network Operators (MVNOs) require efficient and intelligent management to provide reliable services while operating on infrastructure owned by Mobile Network Operators. This project proposes an AI-Driven Autonomous Operations framework for MVNO Networks that uses Artificial Intelligence and Machine Learning to automate network monitoring, traffic prediction, fault detection, resource optimization, and service management. The proposed system analyzes real-time network data to identify performance issues and automatically recommend or initiate appropriate actions. By reducing manual intervention, the framework aims to improve network availability, operational efficiency, service quality, and customer satisfaction while reducing operational costs. The approach supports scalable and intelligent autonomous MVNO operations.
Keywords
Artificial Intelligence Machine Learning Mobile Virtual Network Operator (MVNO) Autonomous Network Operations Network Automation.
Article Information
PUBLICATION DETAILS
Review Type:
Double-Blind Peer Review
License:
CC BY-NC 4.0
Volume/Issue:
Volume 02, Issue 04
Review Rounds:
2
Article Type:
Research Article
Publication Date:
Apr 27 2026
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Cherladine, K. (2026). AI-Driven Autonomous Operations for MVNO Networks. International Journal of Science, Strategic Management and Technology, 02(04), 1-9. https://doi.org/10.55041/ijsmt.v2i4.050
Cherladine, Karthick. "AI-Driven Autonomous Operations for MVNO Networks." International Journal of Science, Strategic Management and Technology, vol. 02, no. 04, 2026, pp. 1-9. doi:https://doi.org/10.55041/ijsmt.v2i4.050.
Cherladine, Karthick. "AI-Driven Autonomous Operations for MVNO Networks." International Journal of Science, Strategic Management and Technology 02, no. 04 (2026): 1-9. https://doi.org/https://doi.org/10.55041/ijsmt.v2i4.050.
References
[1] K. Cherladine, B. R. Rallabandi, A. Goel, K. Kaushik, A. Dhillon, and M. Soni, “Generative adversarial defense models for simulated cyberattack scenarios in virtual networks,” in 2025 International Conference on Digital Innovations for Sustainable …, 2025.
[2] A. Lacava, M. Polese, R. Sivaraj, R. Bendlin, and T. Melodia, “Programmable and Intelligent Open RAN: Opportunities and Challenges,” IEEE Communications Magazine, vol. 61, no. 9, pp. 30–36, 2023.
[3] O-RAN Alliance, “O-RAN Architecture Description,” O-RAN Alliance, 2022.
[4] ] K. Cherladine, “Innovative Telecom: MVNOs and MVNAs Leveraging Cloud-Based Technologies,” International Journal of Computer Trends and Technology, vol. 72, no. 8, pp. 190–198, 2024.
[5] 3GPP, “Study on Artificial Intelligence (AI)/Machine Learning (ML) for Network Management and Orchestration,” 3GPP TR 28.908, Release 18, 2024.
[6] 3GPP, “Management and Orchestration; Concepts, Use Cases and Requirements,” 3GPP TS 28.530, Release 17, 2022.
[7] ETSI, “Zero-Touch Network and Service Management (ZSM),” ETSI GS ZSM, 2022.
[8] ITU-T, “Machine Learning for Future Networks Including 5G,” International Telecommunication Union, 2021.
[9] NGMN Alliance, “5G Network Automation and Intelligence,” NGMN Alliance, 2021.
[10] 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, 2020
[2] A. Lacava, M. Polese, R. Sivaraj, R. Bendlin, and T. Melodia, “Programmable and Intelligent Open RAN: Opportunities and Challenges,” IEEE Communications Magazine, vol. 61, no. 9, pp. 30–36, 2023.
[3] O-RAN Alliance, “O-RAN Architecture Description,” O-RAN Alliance, 2022.
[4] ] K. Cherladine, “Innovative Telecom: MVNOs and MVNAs Leveraging Cloud-Based Technologies,” International Journal of Computer Trends and Technology, vol. 72, no. 8, pp. 190–198, 2024.
[5] 3GPP, “Study on Artificial Intelligence (AI)/Machine Learning (ML) for Network Management and Orchestration,” 3GPP TR 28.908, Release 18, 2024.
[6] 3GPP, “Management and Orchestration; Concepts, Use Cases and Requirements,” 3GPP TS 28.530, Release 17, 2022.
[7] ETSI, “Zero-Touch Network and Service Management (ZSM),” ETSI GS ZSM, 2022.
[8] ITU-T, “Machine Learning for Future Networks Including 5G,” International Telecommunication Union, 2021.
[9] NGMN Alliance, “5G Network Automation and Intelligence,” NGMN Alliance, 2021.
[10] 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, 2020
Ethics and Compliance
✓ All ethical standards met
This article has undergone plagiarism screening and double-blind peer review. Editorial policies have been followed. Authors retain copyright under CC BY-NC 4.0 license. The research complies with ethical standards and institutional guidelines.
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