Radio map (RM) reconstruction is essential for environment-aware wireless networks, but practical measurements are often collected along mobility trajectories rather than randomly scattered over the target region. Such trajectory-sampled observations induce spatially heterogeneous uncertainty: near-trajectory regions are directly constrained, whereas distant or occluded regions remain weakly observed, leading to degraded reconstruction accuracy in under-constrained areas. To address this problem, we propose Trajectory-Guided Plug-and-Play Priors (TGPP), a general guidance module for sparse RM reconstruction. TGPP learns an explicit guidance map as an interpretable input-space risk prior, and an implicit guide feature that is projected and fused with backbone hidden representations. TGPP can be attached to different reconstruction backbones without changing their original task formulation. We further introduce RadioFlow-LDM, a latent flow-based generative backbone, and apply TGPP to deterministic, adversarial, graph-based, and latent generative reconstruction models. Experiments on RadioMapSeer with five trajectory sampling rates show that trajectory-sampled reconstruction differs substantially from random sparse interpolation. TGPP improves most reconstruction metrics across backbones, achieving up to 43.1% NMSE reduction relative to the corresponding base backbone without trajectory-guided priors.

翻译：无线电地图（RM）重建对环境感知型无线网络至关重要，但实际测量数据通常沿移动轨迹采集，而非随机散布于目标区域。这种轨迹采样观测会导致空间异质性不确定性：轨迹附近区域受到直接约束，而远离或遮挡区域则观测薄弱，导致欠约束区域的重建精度下降。针对该问题，我们提出轨迹引导即插即用先验（TGPP），一种面向稀疏RM重建的通用引导模块。TGPP学习显式引导图作为可解释的输入空间风险先验，以及投影并与骨干网络隐藏表征融合的隐式引导特征。TGPP可附加到不同重建骨干网络上，无需改变其原始任务形式。我们进一步引入RadioFlow-LDM，一种基于潜流生成式骨干网络，并将TGPP应用于确定性、对抗性、图基和潜生成式重建模型。在RadioMapSeer上采用五种轨迹采样率的实验表明，轨迹采样重建与随机稀疏插值存在显著差异。TGPP改进了各骨干网络下的大多数重建指标，与无轨迹引导先验的对应骨干网络相比，NMSE降低最高达43.1%。