Author

Rafid Umayer Murshed

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#artificial intelligence Preprint Aug 2026

GCNO: Gramian Chebyshev Neural Operator for Physics-Based Compression of Wireless Channels

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 antenna counts without retraining.

Rafid Umayer Murshed, Shahab Hamidi-Rad, Elahe Soltanaghai et al. · 0 citations

#artificial intelligence Preprint Aug 2026

Physics-Unrolled Neural Operator for Wireless Field Modeling

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, Saif Ur Rahman, Mingyue Tang et al. · 0 citations