WiNeRF: Measurement Constrained Radiance Fields for Actionable Wireless Channel Modeling
Wireless embedded systems increasingly rely on wireless channel information for decision making, yet practical platforms operate under severe constraints, including few antennas, narrow bandwidth, and sparse, noisy measurements. While neural field based approaches inspired by Neural Radiance Fields (NeRFs) have recently been explored for continuous wireless channel modeling, existing approaches depend on dense measurements or external priors such as known geometry, visual context, or angle-of-arrival (AoA) information, limiting their practicality in real-world deployments. We present WiNeRF, a neural field framework that learns a spatially continuous, complex-valued wireless channel repre...
| Authors | Saif Ur Rahman, Rafid Umayer Murshed, Anton Dmitriev et al. |
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| Primary category | cs.NI |
| Published | 24 August 2026 |
| Last updated at source | 24 August 2026 |
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