Integrating AI into the physical layer is a cornerstone of 6G networks. However, current data-driven approaches struggle to generalize across dynamic environments because they lack an intrinsic understanding of electromagnetic wave propagation. We introduce the Wireless World Model (WWM), a multi-modal foundation framework predicting the spatiotemporal evolution of wireless channels by internalizing the causal relationship between 3D geometry and signal dynamics. Pre-trained on a massive ray-traced multi-modal dataset, WWM overcomes the data authenticity gap, further validated under real-world measurement data. Using a joint-embedding predictive architecture with a multi-modal mixture-of-experts Transformer, WWM fuses channel state information, 3D point clouds, and user trajectories into a unified representation. Across the five key downstream tasks supported by WWM, it achieves remarkable performance in seen environments, unseen generalization scenarios, and real-world measurements, consistently outperforming SOTA uni-modal foundation models and task-specific models. This paves the way for physics-aware 6G intelligence that adapts to the physical world.

翻译：将人工智能集成到物理层是6G网络的基石。然而，当前的数据驱动方法由于缺乏对电磁波传播的内在理解，难以在动态环境中泛化。我们提出了无线世界模型（WWM），这是一种多模态基础框架，通过内化三维几何形状与信号动力学之间的因果关系，来预测无线信道的时空演化。通过在大量光线追踪多模态数据集上的预训练，WWM克服了数据真实性的差距，并进一步在真实世界测量数据下得到验证。利用联合嵌入预测架构与多模态混合专家Transformer，WWM将信道状态信息、三维点云和用户轨迹融合为统一表示。在WWM支持的五个关键下游任务中，它在已见环境、未见泛化场景和真实世界测量中均表现卓越，持续优于最先进的单模态基础模型和任务特定模型。这为适应物理世界的物理感知6G智能铺平了道路。