Towards Intent-Based Network Management: Large Language Models for Intent Extraction in 5G Core Networks

• URL: http://arxiv.org/abs/2403.02238v2
• Date: Wed, 22 May 2024 13:34:33 GMT
• Title: Towards Intent-Based Network Management: Large Language Models for Intent Extraction in 5G Core Networks
• Authors: Dimitrios Michael Manias, Ali Chouman, Abdallah Shami,
• Abstract summary: The integration of Machine Learning and Artificial Intelligence into fifth-generation (5G) networks has made evident the limitations of network intelligence. This paper presents the development of a custom Large Language Model (LLM) for 5G and next-generation intent-based networking.
• Score: 10.981422497762837
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 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.

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