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.

翻译：机器学习与人工智能在第五代（5G）网络中的集成，使网络智能在当前及下一代设备日益严苛的需求下显露出局限性。向泛在智能的转型要求用户与网络运营商之间具备高连接性、同步性及端到端通信能力，并将为无需人工干预的全网络自动化铺平道路。意图驱动网络通过转向新型自动化网络管理的提取与解析范式，是减少人工操作、角色与职责的关键要素。本文介绍了面向5G及下一代意图驱动网络的定制化大语言模型（LLM）的开发过程，并探讨了未来LLM的发展方向及其集成方式，以实现端到端意图驱动网络，最终达成全自动化网络智能。