We introduce a self-supervised framework for learning predictive and structured representations of wireless channels by modeling the temporal evolution of channel state information (CSI) in a compact latent space. Our method casts the problem as a world modeling task and leverages the Joint Embedding Predictive Architecture (JEPA) to learn action-conditioned latent dynamics from CSI trajectories. To promote geometric consistency and compositionality, we parameterize transitions using homomorphic updates derived from Lie algebra, yielding a structured latent space that reflects spatial layout and user motion. Evaluations on the DICHASUS dataset show that our approach outperforms strong baselines in preserving topology and forecasting future embeddings across unseen environments. The resulting latent space enables metrically faithful channel charts, offering a scalable foundation for downstream applications such as mobility-aware scheduling, localization, and wireless scene understanding.

翻译：我们提出了一种自监督框架，用于学习无线信道预测性和结构化的表示，通过在紧凑的潜在空间中建模信道状态信息（CSI）的时间演化。该方法将问题建模为世界建模任务，利用联合嵌入预测架构（JEPA）从CSI轨迹中学习基于动作的潜在动力学。为了促进几何一致性和组合性，我们使用基于李代数的同态更新参数化状态转换，从而生成反映空间布局和用户运动的结构化潜在空间。在DICHASUS数据集上的评估表明，我们的方法在保持拓扑结构和跨未见环境预测未来嵌入方面优于强基线模型。由此产生的潜在空间能够生成具有度量保真度的信道图谱，为移动感知调度、定位和无线场景理解等下游应用提供了可扩展的基础。