Revisiting Multi-Level Network Modeling and Simulation With Domain-Adapted LLMs

Abstract

The complexity of modern network infrastructures continues to increase, demanding integrated evaluation across multiple layers—from physical-layer signal propagation to TCP/IP protocol performance. Multi-level network simulators have become indispensable for such analysis, offering cost-effective and risk-free experimentation platforms. However, leveraging these simulators remains challenging due to steep learning curves and extensive domain-specific knowledge requirements. To address this challenge, we introduce a network-oriented Large Language Model (LLM) that serves as an intelligent intermediary between users and simulators, enabling interactive, automated, and script-free simulation workflows. Using NVIDIA Sionna and ns-3 as representative case studies, we adapt a general-purpose LLM into a domain-specialized model through parameter-efficient fine-tuning and retrieval-augmented generation (RAG). The resulting agentic LLM integrates accurate physical-layer modeling with automated higher-layer evaluation, executing complete multi-layer workflows via natural language queries. This framework not only simplifies complex configuration and orchestration tasks but also significantly reduces the barrier to simulation-driven research and education. Our results demonstrate that such network-oriented LLMs can generalize across simulators and network layers, paving the way for a new paradigm of AI-assisted, multi-level network experimentation and design.