用数学结构建模无线信道动态，让机器理解空间运动规律。
Structured Latent Dynamics in Wireless CSI via Homomorphic World Models
- 用李代数构造同态更新，保持信道变化的几何一致性
- 在DICHASUS数据集上预测精度优于基线，跨环境泛化能力强
- 适合做移动感知、定位和无线场景理解的研究者参考
我们提出一种自监督框架，通过在紧凑隐空间中建模信道状态信息（CSI）的时间演化，学习可预测且结构化的无线信道表示。该方法将问题转化为世界建模任务，利用联合嵌入预测架构（JEPA）从CSI轨迹中学习动作条件下的隐状态动态。为提升几何一致性和组合性，采用源自李代数的同态更新参数化转移过程，生成反映空间布局与用户运动的结构化隐空间。在DICHASUS数据集上的评估表明，该方法在保持拓扑结构和预测未来嵌入方面优于强基线，尤其在未见环境中表现优异。所得隐空间支持度量忠实的信道图谱，为移动感知调度、定位及无线场景理解等下游应用提供可扩展基础。
原文摘要 · Abstract (English)
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.
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