Interoperability of AI Agents in Multi-Vendor Telecommunications Networks: An Open Framework for Autonomous Network Coordination
DOI:
https://doi.org/10.63084/algora.v2i2.120Keywords:
AI Agent Interoperability, Multi-Vendor Telecommunications Networks, Autonomous Network Coordination, Multi-Agent Systems, Intent-Based Networking, 5G and 6G Networks, Zero-Touch Network ManagementAbstract
The increasing integration of artificial intelligence (AI) into fifth-generation (5G) and emerging sixth-generation (6G) telecommunications networks has introduced significant challenges in coordinating autonomous agents across heterogeneous, multi-vendor environments. Differences in communication protocols, data representations, operational policies, and decision-making mechanisms can limit interoperability and compromise network management efficiency. This study proposes the Open Framework for Autonomous Network Coordination (OFANC), a five-layer architecture designed to support interoperable communication, semantic consistency, distributed intent negotiation, and policy-aware coordination among heterogeneous AI agents. The framework integrates capability discovery, semantic interoperability, conflict resolution, and secure decision validation across radio access, core, transport, and service management domains. A mathematical optimization model is formulated to balance service-level agreement (SLA) compliance, coordination latency, and communication overhead. A model-based comparative evaluation considers seven network management scenarios and three alternative coordination architectures. The proposed framework achieved an interoperability success rate of 83.58% and an intent fulfillment rate of 79.53%, compared with 75.89% and 63.85%, respectively, for generic agent messaging. It also recorded lower modeled SLA violations and improved conflict resolution, although signaling overhead and fault recovery time increased. These preliminary findings identify potential benefits and operational trade-offs requiring experimental validation. The study provides a standards-aligned architectural foundation for investigating secure, scalable, and coordinated autonomous management in multi-vendor telecommunications networks.
References
1. Atalay, T. O., Maitra, S., Stojadinovic, D., Stavrou, A., & Wang, H. (2025). An OpenRAN security framework for scalable authentication, authorization, and discovery of xApps with isolated critical services. IEEE Transactions on Dependable and Secure Computing, 22(3), 2873–2890. https://doi.org/10.1109/TDSC.2024.3522218
2. Azariah, W., Bimo, F. A., Lin, C.-W., Cheng, R.-G., Nikaein, N., & Jana, R. (2024). A survey on open radio access networks: Challenges, research directions, and open source approaches. Sensors, 24(3), 1038. https://doi.org/10.3390/s24031038
3. Barakabitze, A. A., Ahmad, A., Mijumbi, R., & Hines, A. (2020). 5G network slicing using SDN and NFV: A survey of taxonomy, architectures and future challenges. Computer Networks, 167, 106984. https://doi.org/10.1016/j.comnet.2019.106984
4. Benzaid, C., & Taleb, T. (2020). AI-driven zero touch network and service management in 5G and beyond: Challenges and research directions. IEEE Network, 34(2), 186–194. https://doi.org/10.1109/MNET.001.1900252
5. Bonati, L., D’Oro, S., Polese, M., Basagni, S., & Melodia, T. (2021). Intelligence and learning in O-RAN for data-driven NextG cellular networks. IEEE Communications Magazine, 59(10), 21–27. https://doi.org/10.1109/MCOM.101.2001120
6. Bonati, L., Polese, M., D’Oro, S., Basagni, S., & Melodia, T. (2023). OpenRAN Gym: AI/ML development, data collection, and testing for O-RAN on PAWR platforms. Computer Networks, 220, 109502. https://doi.org/10.1016/j.comnet.2022.109502
7. Coronado, E., Behravesh, R., Subramanya, T., Fernández-Fernández, A., Siddiqui, M. S., Costa-Pérez, X., & Riggio, R. (2022). Zero touch management: A survey of network automation solutions for 5G and 6G networks. IEEE Communications Surveys & Tutorials, 24(4), 2535–2578. https://doi.org/10.1109/COMST.2022.3212586
8. Dryjański, M., Kułacz, Ł., & Kliks, A. (2021). Toward modular and flexible Open RAN implementations in 6G networks: Traffic steering use case and O-RAN xApps. Sensors, 21(24), 8173. https://doi.org/10.3390/s21248173
9. El Rajab, M., Yang, L., & Shami, A. (2024). Zero-touch networks: Towards next-generation network automation. Computer Networks, 243, 110294. https://doi.org/10.1016/j.comnet.2024.110294
10. Ergun, S., Sammour, I., & Chalhoub, G. (2023). A survey on how network simulators serve reinforcement learning in wireless networks. Computer Networks, 234, 109934. https://doi.org/10.1016/j.comnet.2023.109934
11. Fdida, S., Makris, N., Korakis, T., Bruno, R., Passarella, A., Andreou, P., Belter, B., Crettaz, C., Dabbous, W., Demchenko, Y., & Knopp, R. (2022). SLICES, a scientific instrument for the networking community. Computer Communications, 193, 189–203. https://doi.org/10.1016/j.comcom.2022.07.019
12. Foukas, X., Patounas, G., Elmokashfi, A., & Marina, M. K. (2017). Network slicing in 5G: Survey and challenges. IEEE Communications Magazine, 55(5), 94–100. https://doi.org/10.1109/MCOM.2017.1600951
13. S. R. Bej, K. Mann, A. Pakalapati and B. Pillai, "Investigating the Robustness of Machine Learning Models Against Data Poisoning in Industrial Control Systems," 2026 7th International Conference on Intelligent Communication Technologies and Virtual Mobile Networks (ICICV), Tirunelveli, India, 2026, pp. 291-297, doi: 10.1109/ICICV68925.2026.11554655.
14. Gallego-Madrid, J., Sanchez-Iborra, R., Ruiz, P. M., & Skarmeta, A. F. (2022). Machine learning-based zero-touch network and service management: A survey. Digital Communications and Networks, 8(2), 105–123. https://doi.org/10.1016/j.dcan.2021.09.001
15. Gavrilovska, L., Rakovic, V., & Denkovski, D. (2020). From cloud RAN to open RAN. Wireless Personal Communications, 113, 1523–1539. https://doi.org/10.1007/s11277-020-07231-3
16. Giordani, M., Polese, M., Mezzavilla, M., Rangan, S., & Zorzi, M. (2020). Toward 6G networks: Use cases and technologies. IEEE Communications Magazine, 58(3), 55–61. https://doi.org/10.1109/MCOM.001.1900411
17. Groen, J., D’Oro, S., Demir, U., Bonati, L., Polese, M., Melodia, T., & Chowdhury, K. R. (2025). Implementing and evaluating security in O-RAN: Interfaces, intelligence, and platforms. IEEE Network, 39(1), 227–234. https://doi.org/10.1109/MNET.2024.3434419
18. Johnson, D., Maas, D., & Van Der Merwe, J. (2022). NexRAN: Closed-loop RAN slicing in POWDER—A top-to-bottom open-source Open-RAN use case. Proceedings of the 15th ACM Workshop on Wireless Network Testbeds, Experimental Evaluation & Characterization, 17–23. https://doi.org/10.1145/3477086.3480842
19. Koutlia, K., & Lagén, S. (2024). On the impact of Open RAN fronthaul control in scenarios with XR traffic. Computer Networks, 253, 110722. https://doi.org/10.1016/j.comnet.2024.110722
20. Kułacz, Ł., & Kliks, A. (2022). Dynamic spectrum allocation using multi-source context information in OpenRAN networks. Sensors, 22(9), 3515. https://doi.org/10.3390/s22093515
21. Lacava, A., Bonati, L., Mohamadi, N., Gangula, R., Kaltenberger, F., Johari, P., D’Oro, S., Cuomo, F., Polese, M., & Melodia, T. (2025). dApps: Enabling real-time AI-based Open RAN control. Computer Networks, 269, 111342. https://doi.org/10.1016/j.comnet.2025.111342
22. Lacava, A., Polese, M., Sivaraj, R., Soundrarajan, R., Bhati, B. S., Singh, T., Zugno, T., Cuomo, F., & Melodia, T. (2024). Programmable and customized intelligence for traffic steering in 5G networks using Open RAN architectures. IEEE Transactions on Mobile Computing, 23(4), 2882–2897. https://doi.org/10.1109/TMC.2023.3266642
23. Leivadeas, A., & Falkner, M. (2023). A survey on intent-based networking. IEEE Communications Surveys & Tutorials, 25(1), 625–655. https://doi.org/10.1109/COMST.2022.3215919
24. Moudoud, H., Abou El Houda, Z., & Brik, B. (2024). Zero trust security architecture for 6G open radio access networks (ORAN). IEEE Networking Letters, 6(4), 272–275. https://doi.org/10.1109/LNET.2024.3514357
25. Nezami, Z., Zaidi, S. A. R., Hafeez, M., Xu, J., & Djemame, K. (2025). Toward standardization of GenAI-driven agentic architectures for radio access networks. Frontiers in Artificial Intelligence, 8, 1621963. https://doi.org/10.3389/frai.2025.1621963
26. Polese, M., Bonati, L., D’Oro, S., Basagni, S., & Melodia, T. (2023). Understanding O-RAN: Architecture, interfaces, algorithms, security, and research challenges. IEEE Communications Surveys & Tutorials, 25(2), 1376–1411. https://doi.org/10.1109/COMST.2023.3239220
27. Polese, M., Bonati, L., D’Oro, S., Basagni, S., & Melodia, T. (2023). ColO-RAN: Developing machine learning-based xApps for Open RAN closed-loop control on programmable experimental platforms. IEEE Transactions on Mobile Computing, 22(10), 5787–5800. https://doi.org/10.1109/TMC.2022.3188013
28. Rekkas, V. P., Sotiroudis, S., Sarigiannidis, P., Wan, S., Karagiannidis, G. K., & Goudos, S. K. (2021). Machine learning in beyond 5G/6G networks—State-of-the-art and future trends. Electronics, 10(22), 2786. https://doi.org/10.3390/electronics10222786
29. Wadud, A., Golpayegani, F., & Afraz, N. (2024). QACM: QoS-aware xApp conflict mitigation in Open RAN. IEEE Transactions on Green Communications and Networking, 8(3), 978–993. https://doi.org/10.1109/TGCN.2024.3431945
30. S. Rahul, Y. Awati, A. Pakalapati, H. Goyal, S. Tatiya and E. Baskaran, "Deep Triple Spatial Diffractive Neural Network with Dollmaker Optimization Algorithm for Efficient Credit Card Fraud Detection," 2026 12th International Conference on Communication and Signal Processing (ICCSP), Melmaruvathur, India, 2026, pp. 316-321, doi: 10.1109/ICCSP68173.2026.11539095.
31. Wasilewska, M., Kliks, A., Bogucka, H., Cichon, K., Ruseckas, J., Molis, G., Mackutė-Varoneckienė, A., & Kirviliučius, T. (2021). Artificial intelligence for radio communication context-awareness. IEEE Access, 9, 144820–144856. https://doi.org/10.1109/ACCESS.2021.3119524
32. Zhang, Z., Xiao, Y., Ma, Z., Xiao, M., Ding, Z., Lei, X., Karagiannidis, G. K., & Fan, P. (2019). 6G wireless networks: Vision, requirements, architecture, and key technologies. IEEE Vehicular Technology Magazine, 14(3), 28–41. https://doi.org/10.1109/MVT.2019.2921208
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Copyright (c) 2025 Mohammad Mustafa, Mohammad Firoz
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