AirFM-DDA: Air-Interface Foundation Model in the Delay-Doppler-Angle Domain for AI-Native 6G

AirFM-DDA：面向 AI 原生 6G 的时延-多普勒-角度域空口基础模型

Abstract: The success of large foundation models is catalyzing a new paradigm for AI-native 6G network design: wireless foundation models for physical layer design. However, existing models often operate on channel state information (CSI) in the space-time-frequency (STF) domain, where distinct multipath components are inherently superimposed and structurally entangled. This hinders the learning of universal channel representation. Meanwhile, their reliance on global attention mechanisms incurs prohibitive computational overhead.

摘要： 大型基础模型的成功正在催生 AI 原生 6G 网络设计的新范式：用于物理层设计的无线基础模型。然而，现有模型通常在时空频（STF）域处理信道状态信息（CSI），在该域中，不同的多径分量本质上是叠加且结构纠缠的。这阻碍了通用信道表征的学习。同时，它们对全局注意力机制的依赖导致了高昂的计算开销。

In this paper, we propose AirFM-DDA, an Air-interface Foundation Model operating in the Delay-Doppler-Angle (DDA) domain for physical-layer tasks. Specifically, AirFM-DDA reparameterizes CSI from the STF domain into the DDA domain to explicitly resolve multipath components along physically meaningful axes. It employs a window-based attention module augmented with frame-structure-aware positional encoding (FS-PE). This window-based attention aligns with locally clustered multipath dependencies while avoiding quadratic-complexity global attention, and FS-PE injects frame-structure priors into network.

在本文中，我们提出了 AirFM-DDA，这是一种在时延-多普勒-角度（DDA）域运行的物理层任务空口基础模型。具体而言，AirFM-DDA 将 CSI 从 STF 域重新参数化到 DDA 域，以沿着具有物理意义的轴显式解析多径分量。它采用了一种由帧结构感知位置编码（FS-PE）增强的基于窗口的注意力模块。这种基于窗口的注意力机制与局部聚类的多径依赖相一致，同时避免了二次复杂度的全局注意力，而 FS-PE 则将帧结构先验注入到网络中。

Extensive experiments demonstrate that AirFM-DDA achieves superior zero-shot generalization across unseen scenarios and datasets, consistently outperforming the baselines on channel prediction and estimation tasks. Compared to the global attention, its window-based attention reduces training and inference costs by nearly an order of magnitude. Moreover, AirFM-DDA maintains robustness under high mobility, large delay spreads, severe noise, and extreme aliasing conditions.

大量实验表明，AirFM-DDA 在未见过的场景和数据集上实现了卓越的零样本泛化能力，在信道预测和估计任务中持续优于基准模型。与全局注意力相比，其基于窗口的注意力机制将训练和推理成本降低了近一个数量级。此外，AirFM-DDA 在高移动性、大时延扩展、严重噪声和极端混叠条件下仍保持了鲁棒性。