A Wireless World Model for AI-Native 6G Networks
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-...
| Authors | Ziqi Chen, Yi Ren, Yixuan Huang et al. |
|---|---|
| Primary category | cs.NI |
| Published | 26 March 2026 |
| Last updated at source | 14 October 2026 |
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