Revolutionizing Wireless Modeling and Simulation with Network-Oriented LLMs

Jiewen Liu, Zhiyuan Peng, Dongkuan Xu, Yuchen Liu

Abstract: The complexity of modern network infrastructure continues to grow, supporting a wide range of interconnected applications. Simulators are indispensable tools in this context, providing cost-effective and risk-free environments for experimentation and development. However, mastering these network simulators demands substantial domain-specific knowledge, even with comprehensive user manuals. Motivated by the capabilities of Large Language Models (LLMs), this paper introduces the network-oriented LLM as an intermediary between users and network simulators, aiming to offer an interactive, automated, and script-free simulation paradigm. Using the emerging Sionna simulator as a case study, we adapt the general-purpose LLM into a network-oriented LLM through joint parameter-efficient fine-tuning and retrieval-augmented generation, which then streamlines the complex simulation process through simple natural language queries. Comprehensive experiments with state-of-the-art LLMs demonstrate that the proposed method can effectively adapt LLMs for use with network simulators, significantly enhancing user-level operational efficiency and accessibility. The proposed pipeline can facilitate the broader development of various network-oriented LLMs, potentially automating a range of complex network tasks.

External IDs:dblp:conf/ipccc/LiuPXL24