ControlRadio: Prompt-Driven Controllable Diffusion for Cross-Modal Radio Map Generation
2026-08-10 • Computer Vision and Pattern Recognition
Computer Vision and Pattern Recognition
AI summaryⓘ
The authors developed ControlRadio, a new method that creates maps showing how wireless signals travel through an area using descriptions in natural language and details about buildings and transmitter locations. Unlike traditional methods that need lots of measurements or slow simulations, ControlRadio generates accurate and realistic maps much faster. Their approach combines understanding of both the environment layout and signal behavior, making it flexible and reliable across different city scenes. This could help improve wireless communication and sensing by making radio maps easier to produce and use in real time.
radio mapswireless signalsgenerative AInatural language processingenvironmental layoutsignal propagationtransmitter locationsurban wireless networkssimulation-based methodslatent prior
Authors
Kangjun Liu, Xiying Pan, Shuhang Zhang, Xiang Xiang, Ke Chen, Yaowei Wang
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.