Learning Latent Wireless Dynamics From Channel State Information

In this work, we propose a novel data-driven ml technique to model and predict the dynamics of the wireless propagation environment in latent space. Leveraging the idea of channel charting, which learns compressed representations of high-dimensional csi, we incorporate a predictive component to capture the dynamics of the wireless system. Hence, we jointly learn a channel encoder that maps the estimated csi to an appropriate latent space, and a predictor that models the relationships between such representations. Accordingly, our problem boils down to training a jepa that simulates the latent dynamics of a wireless network from csi. We present numerical evaluations on measured data and show that the proposed jepa displays a two-fold increase in accuracy over benchmarks, for longer look-ahead prediction tasks.