RMF: one-step radio map reconstruction via mean flow matching

Accurate radio map construction is essential for 6 G wireless network optimization, yet faces significant challenges due to sparse real-world measurements and dynamic environmental obstacles. This paper presents RMF, a novel one-step generative model based on mean flow matching that enables direct mapping from noise to radio map distribution in a single forward pass. Our approach integrates a multi-feature U-Net architecture with specialized branches for processing building layouts, base station configurations, sparse measurements, and dynamic obstacles through cross-attention fusion. Extensive evaluations on the RadioMapSeer dataset demonstrate that RMF achieves state-of-the-art performance, reducing RMSE by 7.5–12.2% compared to diffusion-based methods while maintaining competitive SSIM scores of 0.9557–0.9674. In challenging zero-measurement scenarios, RMF attains PSNR improvements of 1.45–1.65 dB over existing approaches, showcasing robust performance in both static and dynamic environments. The model's balance of accuracy and efficiency makes it particularly suitable for real-time 6 G applications including coverage optimization and dynamic resource management.