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.
@article{arxiv.2403.02238,
title = {Towards Intent-Based Network Management: Large Language Models for Intent Extraction in 5G Core Networks},
author = {Dimitrios Michael Manias and Ali Chouman and Abdallah Shami},
journal= {arXiv preprint arXiv:2403.02238},
year = {2024}
}