Physics-Guided Deep Unfolding Boosts AI-RAN Channel Prediction
A team of researchers has introduced GUIDE, a physics-informed deep unfolding framework aimed at predicting cross-band channels in AI-native radio access networks (AI-RAN). This innovative approach surpasses current techniques that either excel in various environments or allow for real-time inference, but not both. By integrating wireless channel physics into differentiable layers, GUIDE facilitates generalization without the need for retraining. It delivers a 2.75x improvement in beamforming gain compared to the deep learning benchmark FIRE, with only a minimal increase in inference duration, and achieves a 1.39x gain over the model-based standard R2F2 while operating 1610x faster. This advancement tackles a significant obstacle in the practical application of cross-band channel prediction for AI-RAN.
Key facts
- GUIDE is a physics-guided deep unfolding framework for cross-band channel prediction.
- It embeds wireless channel physics into differentiable layers.
- GUIDE generalizes across diverse environments without retraining.
- It achieves 2.75x beamforming gain over FIRE with slight inference time increase.
- It achieves 1.39x beamforming gain over R2F2 while running 1610x faster.
- Existing approaches fail to achieve both generalization and real-time inference.
- The framework targets AI-native RAN applications.
- The research is from Electrical Engineering and Systems Science > Signal Processing.
Entities
Institutions
- arXiv