Orchestrating Multi-Level Network Modeling and Scientific Simulation with LLMs
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Project Information
- Principal investigator: Dr. Yuchen Liu (PI), Dr. Dongkuan Xu (Co-PI)
- Graduate students: Dongming Wu (NCSU), Jiewen Liu (NCSU)
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Project Overview
- This project introduces a framework that leverages Large Language Models (LLMs) to automate and orchestrate multi-layer network simulations, traditionally performed with distinct and complex tools. By integrating LLMs as agentic intermediaries, we simplify user interaction across physical, packet, and protocol layers of simulation. Each simulator is paired with a customized network-oriented LLM—trained via parameter-efficient fine-tuning and Retrieval-Augmented Generation—to interpret natural language commands. A centralized LLM-based scheduler determines task execution order, while an orchestrator oversees end-to-end coordination and data flow. Using NVIDIA’s Sionna and complementary simulators, our solution enables researchers to model intricate networks without writing scripts or mastering tool-specific configurations. This hierarchical LLM architecture democratizes scientific simulation and streamlines experimentation. The project advances a unified paradigm for automating network system design, with broad implications for domains like 5G/6G, XR, and the industrial IoT.
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