Toward Generative 6G Simulation: An Experimental Multi-Agent LLM and ns-3 Integration

2025-03-17

AI Summary

To address the lack of efficient simulation environments for 6G network research, this paper proposes the first large language model (LLM)-driven generative multi-agent simulation framework. The framework orchestrates multiple autonomous agents via LangChain, integrating structured chain-of-thought (CoT) reasoning with retrieval-augmented generation (RAG) to automatically translate natural-language network specifications into executable ns-3 simulation scripts—enabling end-to-end automation of 5G/6G scenario modeling, simulation execution, and result analysis. Its key contribution lies in breaking away from traditional manual coding paradigms, substantially lowering the barrier to network simulation. Evaluated on the 5G-LENA benchmark, the framework achieves an average convergence of 1.8 iterations per script, a syntax error rate of 17.0%, an average response latency of 7.3 seconds, and a human evaluation score of 7.5/10, demonstrating both effectiveness and practical utility.

Technology Category

• Natural Language Processing: Generation
• Multiagent Systems: Agent-Based Simulation and Emergent Behavior
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Search and Retrieval-Augmented AI: Large language models for search

Abstract

The move toward open Sixth-Generation (6G) networks necessitates a novel approach to full-stack simulation environments for evaluating complex technology developments before prototyping and real-world implementation. This paper introduces an innovative approachfootnote{A lightweight, mock version of the code is available on GitHub at that combines a multi-agent framework with the Network Simulator 3 (ns-3) to automate and optimize the generation, debugging, execution, and analysis of complex 5G network scenarios. Our framework orchestrates a suite of specialized agents -- namely, the Simulation Generation Agent, Test Designer Agent, Test Executor Agent, and Result Interpretation Agent -- using advanced LangChain coordination. The Simulation Generation Agent employs a structured chain-of-thought (CoT) reasoning process, leveraging LLMs and retrieval-augmented generation (RAG) to translate natural language simulation specifications into precise ns-3 scripts. Concurrently, the Test Designer Agent generates comprehensive automated test suites by integrating knowledge retrieval techniques with dynamic test case synthesis. The Test Executor Agent dynamically deploys and runs simulations, managing dependencies and parsing detailed performance metrics. At the same time, the Result Interpretation Agent utilizes LLM-driven analysis to extract actionable insights from the simulation outputs. By integrating external resources such as library documentation and ns-3 testing frameworks, our experimental approach can enhance simulation accuracy and adaptability, reducing reliance on extensive programming expertise. A detailed case study using the ns-3 5G-LENA module validates the effectiveness of the proposed approach. The code generation process converges in an average of 1.8 iterations, has a syntax error rate of 17.0%, a mean response time of 7.3 seconds, and receives a human evaluation score of 7.5.

Problem

• Automates 6G network simulation using multi-agent LLM and ns-3 integration.
• Generates and debugs complex 5G scenarios via structured reasoning and retrieval-augmented generation.
• Enhances simulation accuracy and reduces dependency on programming expertise.

Innovation

• Multi-agent framework integrates LLM with ns-3.
• Automates 5G network scenario generation and analysis.
• Uses CoT reasoning and RAG for script translation.