Accurate and real-time radio map (RM) generation is crucial for next-generation wireless systems, yet diffusion-based approaches often suffer from large model sizes, slow iterative denoising, and high inference latency, which hinder practical deployment. To overcome these limitations, we propose \textbf{RadioFlow}, a novel flow-matching-based generative framework that achieves high-fidelity RM generation through single-step efficient sampling. Unlike conventional diffusion models, RadioFlow learns continuous transport trajectories between noise and data, enabling both training and inference to be significantly accelerated while preserving reconstruction accuracy. Comprehensive experiments demonstrate that RadioFlow achieves state-of-the-art performance with \textbf{up to 8$\times$ fewer parameters} and \textbf{over 4$\times$ faster inference} compared to the leading diffusion-based baseline (RadioDiff). This advancement provides a promising pathway toward scalable, energy-efficient, and real-time electromagnetic digital twins for future 6G networks. We release the code at \href{https://github.com/Hxxxz0/RadioFlow}{GitHub}.

翻译：精确且实时的无线电地图生成对于下一代无线系统至关重要，然而基于扩散的方法通常存在模型规模大、迭代去噪速度慢以及推理延迟高等问题，这阻碍了其实际部署。为克服这些限制，我们提出了 \textbf{RadioFlow}，一种新颖的基于流匹配的生成框架，通过单步高效采样实现高保真无线电地图生成。与传统的扩散模型不同，RadioFlow 学习噪声与数据之间的连续传输轨迹，从而在保持重建精度的同时，显著加速训练和推理过程。综合实验表明，与领先的基于扩散的基线模型相比，RadioFlow 在达到最先进性能的同时，实现了 \textbf{参数数量减少高达 8 倍} 且 \textbf{推理速度提升超过 4 倍}。这一进展为未来 6G 网络的可扩展、高能效和实时电磁数字孪生提供了一条有前景的路径。代码发布于 \href{https://github.com/Hxxxz0/RadioFlow}{GitHub}。