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Towards Intent-Based Network Management: Large Language Models for Intent Extraction in 5G Core Networks (2403.02238v2)
Published 4 Mar 2024 in cs.NI and cs.AI
Abstract: The integration of Machine Learning and Artificial Intelligence (ML/AI) into fifth-generation (5G) networks has made evident the limitations of network intelligence with ever-increasing, strenuous requirements for current and next-generation devices. This transition to ubiquitous intelligence demands high connectivity, synchronicity, and end-to-end communication between users and network operators, and will pave the way towards full network automation without human intervention. Intent-based networking is a key factor in the reduction of human actions, roles, and responsibilities while shifting towards novel extraction and interpretation of automated network management. This paper presents the development of a custom LLM for 5G and next-generation intent-based networking and provides insights into future LLM developments and integrations to realize end-to-end intent-based networking for fully automated network intelligence.
- J. Navarro-Ortiz, P. Romero-Diaz, S. Sendra, P. Ameigeiras, J. J. Ramos-Munoz, and J. M. Lopez-Soler, “A survey on 5g usage scenarios and traffic models,” IEEE Communications Surveys & Tutorials, vol. 22, no. 2, pp. 905–929, 2020.
- A. Chouman, D. M. Manias, and A. Shami, “Towards supporting intelligence in 5g/6g core networks: Nwdaf implementation and initial analysis,” in 2022 International Wireless Communications and Mobile Computing (IWCMC). IEEE, 2022, pp. 324–329.
- ETSI, “Zero-touch network and Service Management (ZSM); Enablers for Artificial Intelligence-based Network and Service Automation,” European Telecommunications Standards Institute (ETSI), Industry Group Specification (IGS) ETSI GS ZSM 012, 2022, version 1.1.1. [Online]. Available: https://www.etsi.org/deliver/etsi_gs/ZSM/001_099/012/01.01.01_60/gs_ZSM012v010101p.pdf
- A. Leivadeas and M. Falkner, “A survey on intent based networking,” IEEE Communications Surveys & Tutorials, 2022.
- L. Velasco, M. Signorelli, O. G. De Dios, C. Papagianni, R. Bifulco, J. J. V. Olmos, S. Pryor, G. Carrozzo, J. Schulz-Zander, M. Bennis et al., “End-to-end intent-based networking,” IEEE communications Magazine, vol. 59, no. 10, pp. 106–112, 2021.
- Y. Njah, A. Leivadeas, J. Violos, and M. Falkner, “Toward intent-based network automation for smart environments: A healthcare 4.0 use case,” IEEE Access, vol. 11, pp. 136 565–136 576, 2023.
- J. Wang, L. Zhang, Y. Yang, Z. Zhuang, Q. Qi, H. Sun, L. Lu, J. Feng, and J. Liao, “Network meets chatgpt: Intent autonomous management, control and operation,” Journal of Communications and Information Networks, vol. 8, no. 3, pp. 239–255, 2023.
- A. M. Da Costa and L. M. C. Murillo, “Integration of network slice controller for enhanced intent-based networking in 5g/6g networks,” in Proceedings of the 18th Workshop on Mobility in the Evolving Internet Architecture, 2023, pp. 31–36.
- K. Abbas, A. Nauman, M. Bilal, J.-H. Yoo, J. W.-K. Hong, and W.-C. Song, “Ai-driven data analytics and intent-based networking for orchestration and control of b5g consumer electronics services,” IEEE Transactions on Consumer Electronics, 2023.
- K. Abbas, T. A. Khan, M. Afaq, and W.-C. Song, “Network slice lifecycle management for 5g mobile networks: An intent-based networking approach,” IEEE Access, vol. 9, pp. 80 128–80 146, 2021.
- J. Mcnamara, D. Camps-Mur, M. Goodarzi, H. Frank, L. Chinchilla-Romero, F. Cañellas, A. Fernández-Fernández, and S. Yan, “Nlp powered intent based network management for private 5g networks,” IEEE Access, 2023.
- D. Wang, R. Su, and S. Zhang, “An intent-based smart slicing framework for vertical industry in b5g networks,” in 2021 IEEE/CIC International Conference on Communications in China (ICCC Workshops). IEEE, 2021, pp. 389–394.
- Y. Wei, M. Peng, and Y. Liu, “Intent-based networks for 6g: Insights and challenges,” Digital Communications and Networks, vol. 6, no. 3, pp. 270–280, 2020.
- 3GPP, “Intent driven management services for mobile networks (Release 18),” 3rd Generation Partnership Project (3GPP), Technical Specification (TS) 28.312, 2023, version 18.1.1. [Online]. Available: https://portal.3gpp.org/desktopmodules/Specifications/SpecificationDetails.aspx?specificationId=3554
- H. Touvron, L. Martin, K. Stone, P. Albert, A. Almahairi, Y. Babaei, N. Bashlykov, S. Batra, P. Bhargava, S. Bhosale et al., “Llama 2: Open foundation and fine-tuned chat models,” arXiv preprint arXiv:2307.09288, 2023.
- D. M. Manias, A. Chouman, and A. Shami, “An nwdaf approach to 5g core network signaling traffic: Analysis and characterization,” in GLOBECOM 2022-2022 IEEE Global Communications Conference. IEEE, 2022, pp. 6001–6006.
- D. M. Manias, A. Chouman, A. Al-Dulaimi, and A. Shami, “Slice-level performance metric forecasting in intelligent transportation systems and the internet of vehicles,” IEEE Internet of Things Magazine, vol. 6, no. 3, pp. 56–61, 2023.
- A. Chouman, D. M. Manias, and A. Shami, “A reliable amf scaling and load balancing framework for 5g core networks,” in 2023 International Wireless Communications and Mobile Computing (IWCMC), 2023, pp. 252–257.
- D. M. Manias, A. Chouman, and A. Shami, “Model drift in dynamic networks,” IEEE Communications Magazine, vol. 61, no. 10, pp. 78–84, 2023.
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