Zero-Shot Frequency Generalization for Radio Map Prediction via Cross-Attention Physics-Residual Learning

Sajjad Hussain

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arXiv · 2608.16194

Zero-Shot Frequency Generalization for Radio Map Prediction via Cross-Attention Physics-Residual Learning

Sajjad Hussain

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Abstract

Deep learning models for radio map prediction are trained and tested at the same carriers and cannot serve unseen frequencies. We propose a two-stream network learning a residual over an analytic prior, fusing environment and physics streams by cross-attention. Evaluated at the query frequency, this free-space and knife-edge prior absorbs dominant frequency scaling of pathloss, leaving a residual that varies little across bands. Across 150 ray-traced scenes and four training carriers (1.8--28 GHz), the method reduces RMSE by 35.3% zero-shot at unseen carriers and scenes, and by 37.6% at extrapolated 60 GHz, outperforming interpolated 10 GHz.

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BibTeX RIS

Sajjad Hussain. 2026-08-17. Zero-Shot Frequency Generalization for Radio Map Prediction via Cross-Attention Physics-Residual Learning. https://arxiv.org/abs/2608.16194

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