发表机构
Hong Kong University of Science and Technology（香港科技大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
Point2Radio是从多环境学习可迁移传播先验的基础模型，基于材料感知点云实现跨场景无线电场预测，在路径增益预测上较UNet基线误差降76.7%，轻量微调可提升环境适应性。
AI 中文摘要
高保真无线电场通常针对每个场景单独进行发射器配置模拟，或单独拟合到每个场景，无法利用不同环境间共享的传播结构。我们提出Point2Radio，一种从多个环境中学习可迁移传播先验的基础模型。给定材料感知点云和发射器（TX）设置，通用编码器生成TX条件下的场景表示，可在任意接收器（RX）位置查询；任务特定查询解码器将该表示映射到不同无线电量，例如三维（3D）路径增益（PG）场和功率角谱（PAS）。在新场景推理时，模型仅需材料感知点云和收发器查询，在单个GPU上运行仅需毫秒级，无需网格或显式路径追踪。我们在包含86272个TX条件场的337个场景语料库的场景不相交划分上评估PG预测，Point2Radio达到0.871 dB的平均绝对误差（MAE），相比同划分的UNet风格基线降低76.7%的误差。同一编码器还可通过任务特定解码器支持PAS预测，实验进一步表明轻量目标场景微调可提升对特定环境的适应性。
英文摘要
High-fidelity radio fields are typically simulated for every scene--transmitter configuration or fitted separately to each scene, failing to exploit propagation structures shared across environments. We present Point2Radio, a foundation model that learns a transferable propagation prior from multiple environments. Given a material-aware point cloud and a transmitter (TX) setting, a common encoder produces a TX-conditioned scene representation that can be queried at arbitrary receiver (RX) locations. Task-specific query decoders map this representation to different radio quantities, e.g., three-dimensional (3D) path-gain (PG) fields and power angular spectra (PAS). At inference for a new scene, the model uses only a material-aware point cloud and transceiver queries, running in milliseconds on a single GPU without meshes or explicit path tracing. We evaluate PG prediction on a scene-disjoint split of a 337-scene corpus containing 86,272 TX-conditioned fields. Point2Radio achieves 0.871 dB mean absolute error (MAE), reducing error by 76.7% relative to a same-split UNet-style baseline. The same encoder also supports PAS prediction via a task-specific decoder. Experiments further show that light target-scene fine-tuning improves adaptation to a specific environment.
