Structured Latent Dynamics in Wireless CSI via Homomorphic World Models
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.
Code (0)
등록된 구현이 없습니다.
Tasks
Scene UnderstandingSimilar Papers 제목 키워드 기반
Learning Ad Hoc Network Dynamics via Graph-Structured World Models
Ad hoc wireless networks exhibit complex, innate and coupled dynamics: node mobility, energy depletion and topology change that are difficult to model analytically. Model-free deep reinforcement learning requires sustain…
Reinforcement LearningDecision MakingDelayed homomorphic reinforcement learning for environments with delayed feedback
Reinforcement learning in real-world systems often involves delayed feedback, which breaks the Markov assumption and impedes both learning and control. Canonical augmentation-based approaches cause state-space explosion,…
Reinforcement LearningCoupled Control and Wireless World Models for Resilient Remote Robotic Control
Remote robotic systems operating over wireless networks must maintain reliable control despite limited communication resources, changing channel conditions, and environmental disturbances.However, continuously transmitti…
MDP Homomorphic Networks: Group Symmetries in Reinforcement Learning
This paper introduces MDP homomorphic networks for deep reinforcement learning. MDP homomorphic networks are neural networks that are equivariant under symmetries in the joint state-action space of an MDP. Current approa…
Deep Reinforcement Learningreinforcement-learningReinforcement LearningReinforcement Learning (RL)Learning Latent Wireless Dynamics from Channel State Information
In this work, we propose a novel data-driven machine learning (ML) technique to model and predict the dynamics of the wireless propagation environment in latent space. Leveraging the idea of channel charting, which learn…