Radio maps (RMs) provide spatially continuous propagation characterizations essential for 6G network planning, but high-fidelity RM construction remains challenging. Rigorous electromagnetic solvers incur prohibitive computational latency, while data-driven models demand massive labeled datasets and generalize poorly from simplified simulations to complex multipath environments. This paper proposes RadioDiff-FS, a few-shot diffusion framework that adapts a pre-trained main-path generator to multipath-rich target domains with only a small number of high-fidelity samples. The adaptation is grounded in a theoretical decomposition of the multipath RM into a dominant main-path component and a directionally sparse residual. This decomposition shows that the cross-domain shift corresponds to a bounded and geometrically structured feature translation rather than an arbitrary distribution change. A Direction-Consistency Loss (DCL) is then introduced to constrain diffusion score updates along physically plausible propagation directions, suppressing phase-inconsistent artifacts that arise in the low-data regime. Experiments show that RadioDiff-FS reduces NMSE by 59.5% on static RMs and by 74.0% on dynamic RMs relative to the vanilla diffusion baseline, achieving an SSIM of 0.9752 and a PSNR of 36.37 dB under severely limited supervision.

翻译：[翻译摘要] 无线地图通过空间连续的传播特性描述为6G网络规划提供关键支撑，但高保真无线地图构建仍面临重大挑战。严格的电磁求解器存在计算延迟过高的问题，而数据驱动模型需要大量标注数据集，且从简化仿真到复杂多径环境的泛化能力较差。本文提出RadioDiff-FS少样本扩散框架，仅需少量高保真样本即可将预训练主路径生成器适配至富含多径效应的目标域。该适配基于多径无线地图的理论分解，将其拆解为主导性主路径分量与方向稀疏残差。此分解表明，跨域偏移对应有界且几何结构化的特征平移，而非任意分布变化。进而引入方向一致性损失，沿物理可行传播方向约束扩散评分更新，抑制低数据场景下产生的相位不一致伪影。实验表明，相较于原始扩散基线，RadioDiff-FS在静态无线地图上使归一化均方误差降低59.5%，动态无线地图降低74.0%，在严格弱监督条件下达到0.9752的结构相似性指数与36.37dB的峰值信噪比。