Towards Intent-Based Network Management: Large Language Models for Intent Extraction in 5G Core Networks
This work addresses the problem of reducing human roles in network management for telecom operators and users, but it appears incremental as it builds on existing intent-based networking concepts with LLMs.
The paper tackles the challenge of automating network management in 5G and next-generation networks by developing a custom Large Language Model for intent extraction, aiming to reduce human intervention and enable full network automation.
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.