# Practical Cross-Band Channel Prediction for AI-RAN via Physics-Guided Deep Unfolding

arXiv: https://arxiv.org/abs/2605.31279 · HTML: https://mlsanity.com/paper/2605.31279/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 0 of its 4 references that are in the map.
- First posted on arXiv 29 May 2026.

## Abstract (arXiv)

To make cross-band channel prediction practical for AI-native RAN, algorithms must generalize across diverse environments and support real-time inference. Existing approaches achieve one but not both. To bridge this gap, we introduce GUIDE, a physics-guided deep unfolding framework that embeds wireless channel physics into differentiable layers. Without retraining in unseen environments, GUIDE achieves 2.75x beamforming gain than the deep learning-based baseline FIRE with only a slight increase in inference time, and 1.39x beamforming gain than the strongest model-based baseline R2F2 while running over 1610x faster.