A Wireless World Model for AI-Native 6G Networks

TL;DR

This paper introduces the Wireless World Model (WWM), a physics-aware AI framework for 6G networks that predicts wireless channel evolution by integrating electromagnetic principles, enabling better generalization and real-world performance.

Contribution

The paper presents WWM, a novel multi-modal foundation model that internalizes electromagnetic physics for wireless channel prediction, surpassing existing models in generalization and real-world validation.

Findings

Outperforms state-of-the-art models in multiple downstream tasks

Achieves strong generalization to unseen environments

Validated with real-world measurement data

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.