Practical Cross-Band Channel Prediction for AI-RAN via Physics-Guided Deep Unfolding
This work is significant for AI-native RAN developers and researchers, as it provides a method that generalizes across environments and supports real-time inference for cross-band channel prediction, which is an incremental improvement over existing methods.
This paper addresses the challenge of practical cross-band channel prediction for AI-native RAN, where existing methods struggle with both generalization and real-time inference. The authors introduce GUIDE, a physics-guided deep unfolding framework, which achieves 2.75x beamforming gain over a deep learning baseline and 1.39x gain over a model-based baseline, while also being significantly faster.
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.