WiNeRF is presented, a neural field framework that learns a spatially continuous, complex-valued wireless channel representation directly from sparse channel state information (CSI) collected by commodity WiFi devices, and produces a task-agnostic channel representation that can be directly reused in standard signal-pr...
S. ur Rahman, Rafid Umayer Murshed, A. Dmitriev et al.· 0 citations
The Gramian Chebyshev Neural Operator is introduced, a physics-based, variable-rate compressor that identifies a sample-dependent set of path directions that achieves better reconstruction accuracy at the same payload - or lower payload at the same accuracy - than neural feedback baselines, and transfers to unseen ante...
This work proposes Physics-Unrolled Hybrid Neural Operator (PU-HNO), a three-stage cascade that predicts high-fidelity indoor radio maps from low-fidelity ray-tracing outputs and scene priors by progressively capturing reflection, diffraction, and scattering effects, rather than treating radio maps as generic images.
Rafid Umayer Murshed, S. ur Rahman, Mingyue Tang et al.· 0 citations
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