A Wireless World Model for AI-Native 6G Networks
Ziqi Chen Yi Ren Yixuan Huang Qi Sun Nan Li Yuhong Huang Yifan Li Liang Xia
作者信息
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 models. This paves the way for physics-aware 6G intelligence that adapts to the physical world.引用本文复制引用
Ziqi Chen,Yi Ren,Yixuan Huang,Qi Sun,Nan Li,Yuhong Huang,Yifan Li,Liang Xia.A Wireless World Model for AI-Native 6G Networks[EB/OL].(2026-10-14)[2026-10-24].https://arxiv.org/abs/2603.25216.学科分类
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