RadioSim Agent: Combining Large Language Models and Deterministic EM Simulators for Interactive Radio Map Analysis

TL;DR

RadioSim Agent integrates large language models with physics-based electromagnetic simulators to enable interactive, explainable, and multimodal radio map analysis for wireless system design.

Contribution

It introduces an agentic framework combining LLMs and EM simulators for interactive radio map generation and analysis, enhancing interpretability and user interaction.

Findings

Autonomous selection of propagation mechanisms

Execution of deterministic EM simulations

Provision of semantic and visual summaries

Abstract

Deterministic electromagnetic (EM) simulators provide accurate radio propagation modeling but often require expert configuration and lack interactive flexibility. We present RadioSim Agent, an agentic framework that integrates large language models (LLMs) with physics-based EM solvers and vision-enabled reasoning to enable interactive and explainable radio map generation. The framework encapsulates ray-tracing models as callable simulation tools, orchestrated by an LLM capable of interpreting natural language objectives, managing simulation workflows, and visually analyzing resulting radio maps. Demonstrations in urban UAV communication scenarios show that the agent autonomously selects appropriate propagation mechanisms, executes deterministic simulations, and provides semantic and visual summaries of pathloss behavior. The results indicate that RadioSim Agent provides multimodal interpretability and intuitive user interaction, paving the way for intelligent EM simulation assistants in next-generation wireless system design.