Abstract: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 $N\times$.
| Subjects: | Networking and Internet Architecture (cs.NI) |
| Cite as: | arXiv:2605.24290 [cs.NI] |
| (or arXiv:2605.24290v1 [cs.NI] for this version) | |
| https://doi.org/10.48550/arXiv.2605.24290 arXiv-issued DOI via DataCite (pending registration) |
From: Kang Yang [view email]
[v1]
Fri, 22 May 2026 23:44:40 UTC (1,105 KB)
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