Intent-Based Autonomous Network Framework Guided by Large Language Model

With the rapid development of next-generation networks, the highly heterogeneous and dynamic nature of networks poses significant challenges for automated network management. Autonomous Network (AN), as a new network paradigm, aims to provide customers with a zero-wait, zero-touch, and zero-fault experience. AN facilitates network management through intent-driven interactions and provides on-demand resource orchestration and service scheduling. However, accurately translating user intents into commands and allocating resources on demand for services remain significant challenges for AN. Therefore, this paper proposes IAN, an intent-based AN framework guided by the Large Language Model (LLM). In the intent translation phase, IAN introduces RAG to enhance command generation quality by retrieving from manuals. In the resource allocation phase, the method utilizes LLM to analyze service characteristics, thereby guiding the training and inference of the resource allocation model to effectively distribute resources uniformly across emerging services. Experimental results demonstrate that IAN improves performance by 52.66% in intent translation tasks and increases overall gain by 33.57% in resource allocation tasks compared to other models.