发表机构
Pengcheng Laboratory; South China University of Technology; Peking University; Huazhong University of Science and Technology; Harbin Institute of Technology, Shenzhen（鹏城实验室; 华南理工大学; 北京大学; 华中科技大学; 哈尔滨工业大学（深圳））
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
ControlRadio是一种可控生成框架，可根据自然语言描述和环境布局生成无线电地图，在不同城市场景中精度和泛化能力领先，计算时间较传统方法减少四个数量级以上，为无线环境建模提供新范式。
AI 中文摘要
无线电地图描述无线信号在空间中的传播情况，对无线通信、传感和网络规划至关重要。然而，传统构建准确无线电地图的方法要么需要密集测量，要么需要计算成本高昂的物理模拟，这限制了其可扩展性和实时部署。生成式人工智能的最新进展提供了一种有前景的替代方案，但现有方法在应用于真实无线环境时缺乏细粒度控制和物理一致性。本文提出ControlRadio，这是一种可控生成框架，可根据自然语言描述和环境布局（包括建筑结构和发射机位置）生成无线电地图。联合语义和空间条件实现了可解释、符合传播规律的生成，而可控潜在先验和感知布局的条件则提高了稳定性和结构一致性。大量实验表明，ControlRadio在不同城市场景中达到了最先进的精度和较强的泛化能力，同时与传统基于模拟的方法相比，计算时间减少了四个数量级以上。这些结果表明，这为可扩展且可控的无线环境建模提供了新范式，对下一代通信系统和数据驱动的无线电传感具有广泛意义。
英文摘要
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.
Comments17 pages, 9 figures, 8 tables
