Accurate radio map (RM) construction is essential for 6G wireless network optimization, yet faces significant challenges owing to sparse real-world measurements and dynamic environmental obstacles. This paper presents RMF, a novel single-step generative model based on mean flow matching that enables direct mapping from a noise prior to the target radio map distribution in a single forward pass, eliminating the iterative inference required by diffusion-based approaches. The proposed model integrates a multi-feature U-Net backbone with four specialized branches that extract and fuse building-layout features—via dual-path frequency and spatial-domain processing—base station distance fields, graph neural network-encoded sparse measurements, and dynamic obstacle representations, all injected through multi-scale cross-attention. Evaluations on the RadioMapSeer benchmark show that RMF attains the best RMSE and PSNR among the compared methods, with RMSE between 0.0136 and 0.0162 and PSNR between 36.52 and 37.24 dB, SSIM within 0.012 of the leading diffusion baseline, and an order-of-magnitude reduction in per-sample inference time. In the challenging zero-measurement scenario, RMF achieves PSNR gains of 1.45–1.55 dB over competing methods in both static and dynamic environments. The single forward-pass design yields inference times of 0.05 s, making RMF a promising candidate for real-time 6G applications such as coverage optimization and dynamic spectrum management, subject to validation on field-measured data in future work.
Lei et al. (Fri,) studied this question.