Towards Intent-Based Network Management: Large Language Models for Intent Extraction in 5G Core Networks
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arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866909208798035968 |
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| author | Manias, Dimitrios Michael Chouman, Ali Shami, Abdallah |
| author_facet | Manias, Dimitrios Michael Chouman, Ali Shami, Abdallah |
| contents | 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 Large Language Model (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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_02238 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Towards Intent-Based Network Management: Large Language Models for Intent Extraction in 5G Core Networks Manias, Dimitrios Michael Chouman, Ali Shami, Abdallah Networking and Internet Architecture Artificial Intelligence 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 Large Language Model (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. |
| title | Towards Intent-Based Network Management: Large Language Models for Intent Extraction in 5G Core Networks |
| topic | Networking and Internet Architecture Artificial Intelligence |
| url | https://arxiv.org/abs/2403.02238 |