RadioSim Agent: Combining Large Language Models and Deterministic EM Simulators for Interactive Radio Map Analysis
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arXiv
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866908638622253056 |
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| author | Hussain, Sajjad Brennan, Conor |
| author_facet | Hussain, Sajjad Brennan, Conor |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_05912 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | RadioSim Agent: Combining Large Language Models and Deterministic EM Simulators for Interactive Radio Map Analysis Hussain, Sajjad Brennan, Conor Signal Processing 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. |
| title | RadioSim Agent: Combining Large Language Models and Deterministic EM Simulators for Interactive Radio Map Analysis |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2511.05912 |