Radio-frequency (RF) data synthesis predicts the received signal given transmitter and receiver positions, and is essential for wireless applications. Recent 3D Gaussian Splatting (3DGS)-based methods achieve efficient synthesis at any transmitter but only for a fixed receiver. Therefore, supporting $N$ receivers in one scene requires $N$ independent models and precludes prediction at unseen receivers. We present RxGS, which achieves receiver-generalizable synthesis within a single unified model. Our key insight is that scene geometry is receiver-independent while directional radiance is not: a first stage learns shared 3D Gaussian geometry, and a second stage freezes it and learns directional radiance conditioned on receiver position. A global conditioning branch captures shared receiver-dependent effects across the scene, while a local branch models per-scatterer variations from the receiver's geometry and occlusion. A multi-receiver CUDA rasterizer further batches rendering across all $N$ receivers. Evaluated across various RF datasets, RxGS matches or improves over per-receiver baselines with a single shared model and generalizes to receivers unseen during training within the scene, cutting training cost by up to $45\times$, inference cost by $7.6\times$, and storage by $N\times$.

翻译：摘要：射频数据合成根据发射器和接收器位置预测接收信号，是无线应用的关键技术。近期基于三维高斯泼溅（3DGS）的方法虽能高效合成任意发射器的信号，但仅支持固定接收器。因此，在一个场景中支持N个接收器需要N个独立模型，且无法预测未训练接收器位置的信号。我们提出RxGS，该方法可在单一统一模型中实现接收器泛化的信号合成。核心洞察在于：场景几何结构独立于接收器，而方向辐射则与之相关——第一阶段学习共享的三维高斯几何，第二阶段冻结几何参数并学习以接收器位置为条件的方向辐射。全局条件分支捕捉场景中共享的接收器相关效应，局部分支则根据接收器几何与遮挡建模各散射体的变化。多接收器CUDA光栅化器进一步将N个接收器的渲染过程批量处理。在多个射频数据集上的评估表明，RxGS采用单一共享模型即可匹配或超越逐接收器基线性能，并能泛化至场景内训练中未见的接收器位置，将训练成本降低至原来的1/45，推理成本降至1/7.6，存储成本降至1/N。