ControlRadio: Prompt-Driven Controllable Diffusion for Cross-Modal Radio Map Generation

2026-08-10

AI Summary

This work addresses the longstanding challenge in wireless signal map construction, where conventional approaches relying on dense measurements or high-fidelity physical simulations struggle to balance scalability, real-time performance, and physical consistency. To overcome these limitations, the authors propose ControlRadio—the first controllable diffusion framework capable of jointly leveraging natural language prompts and environmental layouts (e.g., building structures and transmitter locations) to guide signal map synthesis. By integrating layout-aware conditioning mechanisms and a controllable latent-space prior, ControlRadio generates semantically interpretable and structurally coherent signal maps while preserving electromagnetic propagation fidelity. Extensive experiments demonstrate that ControlRadio achieves state-of-the-art accuracy across diverse urban scenarios, exhibits strong generalization capabilities, and accelerates inference by over four orders of magnitude compared to traditional simulation-based methods.

Abstract

Radio maps describe how wireless signals propagate across space and are essential for wireless communication, sensing, and network planning. However, constructing accurate radio maps traditionally requires either dense measurements or computationally expensive physical simulations, which limits scalability and real-time deployment. Recent advances in generative artificial intelligence offer a promising alternative, but existing approaches lack fine-grained control and physical consistency when applied to real-world wireless environments. Here we present \textbf{ControlRadio}, a controllable generative framework that produces radio maps from natural-language descriptions and environmental layouts, including building structures and transmitter locations. Joint semantic and spatial conditioning enables interpretable, propagation-plausible generation, while a controlled latent prior and layout-aware conditioning improve stability and structural consistency. Extensive experiments demonstrate that ControlRadio achieves state-of-the-art accuracy and strong generalization across diverse urban scenarios, while reducing computation time by more than four orders of magnitude compared with conventional simulation-based methods. Such results suggest a new paradigm for scalable and controllable wireless environment modeling, with broad implications for next-generation communication systems and data-driven radio sensing.

Problem

• radio map generation
• controllable generation
• cross-modal learning
• wireless environment modeling
• physical consistency

Innovation

• controllable diffusion
• radio map generation
• cross-modal generation
• layout-aware conditioning
• prompt-driven AI