[2603.25216] A Wireless World Model for AI-Native 6G Networks

Computer Science > Networking and Internet Architecture arXiv:2603.25216 (cs) [Submitted on 26 Mar 2026] Title:A Wireless World Model for AI-Native 6G Networks Authors:Ziqi Chen, Yi Ren, Yixuan Huang, Qi Sun, Nan Li, Yuhong Huang, Chih-Lin I, Yifan Li, Liang Xia View a PDF of the paper titled A Wireless World Model for AI-Native 6G Networks, by Ziqi Chen and 8 other authors View PDF HTML (experimental) Abstract: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 model...