融合无线物理规律的深度展开框架，实现跨频段信道预测的高效高泛化。
Practical Cross-Band Channel Prediction for AI-RAN via Physics-Guided Deep Unfolding
- 将无线信道物理规律嵌入可微分层，构建物理引导的深度展开模型。
- 在未见过环境中比DL基线提升2.75倍波束成形增益，推理延迟仅略增。
- 比最优模型基线快1610倍，适合实时AI-RAN场景部署。
为使AI原生无线接入网（AI-RAN）中的跨频段信道预测具备实用性，算法必须在多样环境间保持泛化能力并支持实时推理。现有方法难以兼顾二者。为此，本文提出GUIDE——一种融合无线信道物理规律的深度展开框架，将物理知识嵌入可微分层。在无需重新训练的未知环境中，GUIDE相比基于深度学习的基线FIRE实现2.75倍波束成形增益，且推理时间仅略有增加；同时相比最强的模型基线R2F2实现1.39倍增益，运行速度超过其1610倍。
原文摘要 · Abstract (English)
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.
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