Neural Gaussian Radio Fields for Channel Estimation
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866911435924176896 |
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| author | Umer, Muhammad Mohsin, Muhammad Ahmed Bilal, Ahsan Cioffi, John M. |
| author_facet | Umer, Muhammad Mohsin, Muhammad Ahmed Bilal, Ahsan Cioffi, John M. |
| contents | Accurate channel state information (CSI) is a critical bottleneck in modern wireless networks, with pilot overhead consuming 11\% to 21\% of transmission bandwidth and feedback delays causing severe throughput degradation under mobility. Addressing this requires rethinking how neural fields represent coherent wave phenomena. This work introduces \textit{neural Gaussian radio fields (\textcolor{stanfordred}{nGRF})}, a physics-informed framework that fundamentally reframes neural field design by replacing view-dependent rasterization with direct complex-valued aggregation in 3D space. This approach natively models wave superposition rather than visual occlusion. The architectural shift transforms the learning objective from function-fitting to source-recovery, a well-posed inverse problem grounded in electromagnetic theory. While demonstrated for wireless channel estimation, the core principle of explicit primitive-based fields with physics-constrained aggregation extends naturally to any coherent wave-based domain, including acoustic propagation, seismic imaging, and ultrasound reconstruction. Evaluations show that the inductive bias of \textcolor{stanfordred}{nGRF} achieves 10.9 dB higher prediction SNR than state-of-the-art methods with 220$\times$ faster inference (1.1 ms vs. 242 ms), 18$\times$ lower measurement density, and 180$\times$ faster training. For large-scale outdoor environments where implicit methods fail, \textcolor{stanfordred}{nGRF} achieves 28.32 dB SNR, demonstrating that structured representations supplemented by domain physics can fundamentally outperform generic deep learning architectures. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_11668 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Neural Gaussian Radio Fields for Channel Estimation Umer, Muhammad Mohsin, Muhammad Ahmed Bilal, Ahsan Cioffi, John M. Signal Processing Networking and Internet Architecture Accurate channel state information (CSI) is a critical bottleneck in modern wireless networks, with pilot overhead consuming 11\% to 21\% of transmission bandwidth and feedback delays causing severe throughput degradation under mobility. Addressing this requires rethinking how neural fields represent coherent wave phenomena. This work introduces \textit{neural Gaussian radio fields (\textcolor{stanfordred}{nGRF})}, a physics-informed framework that fundamentally reframes neural field design by replacing view-dependent rasterization with direct complex-valued aggregation in 3D space. This approach natively models wave superposition rather than visual occlusion. The architectural shift transforms the learning objective from function-fitting to source-recovery, a well-posed inverse problem grounded in electromagnetic theory. While demonstrated for wireless channel estimation, the core principle of explicit primitive-based fields with physics-constrained aggregation extends naturally to any coherent wave-based domain, including acoustic propagation, seismic imaging, and ultrasound reconstruction. Evaluations show that the inductive bias of \textcolor{stanfordred}{nGRF} achieves 10.9 dB higher prediction SNR than state-of-the-art methods with 220$\times$ faster inference (1.1 ms vs. 242 ms), 18$\times$ lower measurement density, and 180$\times$ faster training. For large-scale outdoor environments where implicit methods fail, \textcolor{stanfordred}{nGRF} achieves 28.32 dB SNR, demonstrating that structured representations supplemented by domain physics can fundamentally outperform generic deep learning architectures. |
| title | Neural Gaussian Radio Fields for Channel Estimation |
| topic | Signal Processing Networking and Internet Architecture |
| url | https://arxiv.org/abs/2508.11668 |