Deep learning (DL) has been widely used in future 6G physical layer communications, but task-specific DL models are difficult to generalize across different physical layer tasks. Recently emerging wireless foundation models demonstrate strong generalization capability. However, existing methods mainly adapt pretrained language/vision models or rely on CSI reconstruction objectives for pretraining, with limited use of channel knowledge, and thus have limited performance. To address this limitation, we propose SPA-MAE, a physics-guided wireless foundation model by exploiting the adapted MAE backbone and channel knowledge. A physical prior module is developed to provide two complementary guidance signals in the pretraining stage. Specifically, the parameter-aware guidance branch extracts features from explicit multipath parameters and encourages the encoder output to align them, while the structure-aware guidance branch encourages the encoder to capture the sparse transformed-domain CSI structure obtained after a 2D FFT. After end-to-end learning, the MAE encoder will be retained for downstream tasks. Experiments on four wireless tasks show that SPA-MAE outperforms state-of-the-art CSI foundation models with smaller number of parameters, especially under low-SNR and limited-data conditions.

翻译：深度学习已在未来6G物理层通信中广泛应用，但特定任务的深度学习模型难以泛化至不同物理层任务。近期涌现的无线基础模型展现出强大的泛化能力，然而现有方法主要依赖预训练语言/视觉模型的迁移或基于信道状态信息重建目标的预训练，对信道知识的利用有限，导致性能受限。为解决这一问题，我们提出SPA-MAE——一种通过适配MAE主干网络与信道知识的物理引导无线基础模型。该模型在预训练阶段开发了一个物理先验模块，提供两种互补引导信号：参数感知引导分支从显式多径参数中提取特征，并促使编码器输出与其对齐；结构感知引导分支则引导编码器捕获经二维快速傅里叶变换后稀疏的变换域信道状态信息结构。经过端到端学习后，MAE编码器将保留用于下游任务。在四个无线任务上的实验表明，SPA-MAE以更少的参数量优于现有最先进的信道状态信息基础模型，尤其在低信噪比与有限数据条件下表现更为突出。