ArXiv

PropSplat: Map-Free RF Field Reconstruction via 3D Gaussian Propagation Splatting

2026-05-08 · 17:24 UTC ·William Bjorndahl, Maninder Pal Singh, Farhad Nouri... ·1 min read

Authors: William Bjorndahl, Maninder Pal Singh, Farhad Nouri...
Categories: eess.SP, cs.LG
arXiv: https://arxiv.org/abs/2605.08035v1
PDF: https://arxiv.org/pdf/2605.08035v1

Brief

PropSplat addresses site-specific RF field reconstruction without maps by representing propagation structure as 3D anisotropic Gaussians that learn per-primitive path-loss offsets plus a global path-loss exponent. Trained end-to-end from sparse transmitter–receiver measurements, it outperforms NeRF^2, GSRF, and WRF-GS+ on outdoor drive-tests (5.38 dB RMSE at 300 m spacing) and yields sub-meter indoor localization (0.19 m). Accepted to IEEE DySPAN 2026.

Why it matters

PropSplat is a map-free RF propagation method that models environments with optimized 3D anisotropic Gaussian primitives; each Gaussian encodes a scalar path-loss offset relative to a baseline path-loss model with a learnable path-loss exponent, initialized along observed transmitter–receiver paths and optimized end-to-end without geographic data.

Key details

On large outdoor drive-tests across multiple topographical regions at six sub-6 GHz frequencies with training measurements spaced 300 m, PropSplat achieves 5.38 dB RMSE (vs WRF-GS+ 5.87 dB, GSRF 7.46 dB, NeRF^2 14.76 dB); on indoor BLE measurements it attains 0.19 m mean localization error vs NeRF^2's 1.84 m while delivering near-identical RSS prediction accuracy.

Source evidence

Abstract

Building a site-specific propagation model typically requires either ray-tracing over detailed 3D maps or dense measurement campaigns. Both approaches are expensive and often infeasible for rapid deployments where geographic data is unavailable or outdated. We present PropSplat, a map-free propagation modeling method that reconstructs radio frequency (RF) fields using 3D anisotropic Gaussian primitives. Each Gaussian encodes a scalar path loss offset relative to an explicit baseline path loss model with a learnable path loss exponent. Gaussians are initialized along observed transmitter--receiver paths and optimized end-to-end to learn the propagation environment without external information like floor plans, terrain databases, or clutter data. We evaluate PropSplat against wireless radiance field methods NeRF$^2$, GSRF, and WRF-GS+ on two real-world datasets. On large-scale outdoor drive-tests spanning multiple topographical regions at six sub-6 GHz frequencies, PropSplat achieves 5.38 dB RMSE when training measurements are spaced 300m apart and outperforms WRF-GS+ (5.87 dB), GSRF (7.46 dB), and NeRF$^2$ (14.76 dB). On indoor Bluetooth Low Energy measurements, PropSplat achieves 0.19m mean localization error, an order of magnitude better than NeRF$^2$ (1.84m), while achieving near-identical received signal strength prediction accuracy. These results show that accurate site-specific propagation reconstruction is achievable from sparse RF-native measurements. The need for geographic data as a prerequisite for scalable RF environment modeling is reduced.

Comment: Accepted for presentation at IEEE DySPAN 2026