用物理规律建模无线信道演化，让6G网络更懂真实世界。
A Wireless World Model for AI-Native 6G Networks
- 融合3D几何与信号动态，内化电磁波传播因果关系。
- 在5个下游任务中超越现有模型，在真实场景表现优异。
- 适合研究6G智能、无线感知与物理驱动AI的学者。
将AI融入物理层是6G网络的核心。然而，当前数据驱动方法在动态环境中泛化能力差，因其缺乏对电磁波传播的内在理解。本文提出无线世界模型（WWM），一种多模态基础框架，通过内化三维几何与信号动态之间的因果关系，预测无线信道的时空演化。WWM在大规模射线追踪多模态数据集上预训练，克服了数据真实性差距，并在真实测量数据中得到验证。采用联合嵌入预测架构与多模态专家混合Transformer，WWM融合信道状态信息、三维点云和用户轨迹，形成统一表征。在支持的五个关键下游任务中，无论在已见环境、未见泛化场景还是真实测量中，性能均显著优于最先进单模态基础模型与专用模型。这为具备物理感知的6G智能提供了新路径。
原文摘要 · Abstract (English)
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.
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