Title: Neural Gaussian Radio Fields for Channel Estimation
Primary area: applications to physical sciences (physics, chemistry, biology, etc.)
Keywords: neural fields, channel estimation, Gaussian splatting, NeRF

Abstract: Accurate channel state information (CSI) is a significant bottleneck in modern wireless networks, with pilot overhead consuming 11-21\% of transmission bandwidth and feedback delays causing severe throughput degradation under mobility. To address this, this work introduces a new class of neural fields designed for coherent wave-based phenomena, called neural Gaussian radio fields (nGRF), which combines the efficiency of explicit primitive-based representations with a novel differentiable operator. nGRF replaces view-dependent, computer graphics-centric rasterization with direct, complex-valued aggregation in 3D space that natively models wave superposition and interference. Consequently, this reframes the learning objective from a function-fitting task to a well-posed source-recovery problem. In evaluations, nGRF demonstrates superior performance across diverse environments. In indoor scenarios, it achieves 10.9 dB higher prediction SNR than state-of-the-art methods while reducing inference latency from 242 ms to 1.1 ms (a 220$\times$ speedup). For large-scale outdoor environments where existing approaches fail, nGRF achieves an SNR of 26.2 dB. Furthermore, the proposed method reduces the required measurement density by 18$\times$ (0.011 vs. 0.2-178.1 measurements/ft$^3$) and cuts the training time from hours to minutes (a 180$\times$ reduction), enabling rapid adaptation to dynamic environments. The code and datasets are available at https://github.com/anonym-auth/n-grf.

TL;DR: This work introduces nGRF, a framework using explicit 3D Gaussian primitives for MIMO channel estimation by directly modeling the physical superposition of electromagnetic waves, achieving state-of-the-art accuracy with a 220x speedup.