AI 中文总结
研究针对现有3D无线电地图构建方法对高度维度建模不足的问题，提出BDFlow-3DRM框架，通过双动态流匹配，从收发器高度输入学习表示，建模相邻层依赖关系，实验验证其有效性，相比基线降低误差与复杂度，有实际构建潜力。
AI 中文摘要
三维（3D）无线电地图（RM）是低空无线场景中环境感知通信的关键推动因素，可提供按空间位置索引的特定地点信道先验信息。然而，现有的3D RM构建方法缺乏对高度维度的有效建模，限制了其对未见高度配置的泛化能力，并降低了跨高度层的构建连贯性。本文提出了BDFlow-3DRM，一种用于3D RM构建的双动态流匹配框架。具体而言，3D RM构建问题被表述为语义潜在空间中的确定性概率流。BDFlow-3DRM从灵活的收发器高度输入中学习连续的高度感知表示，增强几何感知。同时，其双动态设计明确地对相邻高度层之间的双向依赖关系进行建模，以便可以联合构建不同高度层的RM。在多个数据集上进行的广泛实验，涵盖了各种简化和现实的城市场景，验证了BDFlow-3DRM的有效性。与基于扩散的基线相比，它将归一化均方误差（NMSE）降低了28.6%，并将推理复杂度降低了180倍。更重要的是，仅使用20个训练接收器高度层，BDFlow-3DRM在可变发射机高度下，在1至120米的宽连续接收器高度范围内保持准确预测，突出了其在大规模3D RM构建中的实际潜力。
英文摘要
Three-dimensional (3D) radio map (RM) is a key enabler for environment-aware communications in low-altitude wireless scenarios by providing site-specific channel priors indexed by spatial locations. However, existing 3D RM construction methods lack effective modeling of the height dimension, which limits their generalization to unseen height configurations and degrades construction coherence across height layers. In this paper, we propose BDFlow-3DRM, a bi-dynamical flow matching framework for 3D RM construction. Specifically, the 3D RM construction problem is formulated as a deterministic probability flow in a semantic latent space. BDFlow-3DRM learns continuous height-aware representations from flexible transceiver height inputs, enhancing geometric awareness. Meanwhile, its bi-dynamical design explicitly models bidirectional dependencies across neighboring height layers, so that RMs at different height layers can be constructed jointly. Extensive experiments on multiple datasets, covering diverse simplified and realistic urban scenarios, validate the effectiveness of BDFlow-3DRM. Compared with diffusion-based baselines, it reduces the normalized mean square error (NMSE) by 28.6% and attains a 180-fold reduction in inference complexity. More importantly, with only 20 training receiver-height layers, BDFlow-3DRM maintains accurate prediction over a wide continuous receiver-height range from 1 to 120 m under variable transmitter heights, highlighting its practical potential for large-scale 3D RM construction.
