  Channel charting (CC) applies dimensionality reduction to channel state
information (CSI) data at the infrastructure basestation side with the goal of
extracting pseudo-position information for each user. The self-supervised
nature of CC enables predictive tasks that depend on user position without
requiring any ground-truth position information. In this work, we focus on the
practically relevant streaming CSI data scenario, in which CSI is constantly
estimated. To deal with storage limitations, we develop a novel streaming CC
architecture that maintains a small core CSI dataset from which the channel
charts are learned. Curation of the core CSI dataset is achieved using a
min-max-similarity criterion. Numerical validation with measured CSI data
demonstrates that our method approaches the accuracy obtained from the complete
CSI dataset while using only a fraction of CSI storage and avoiding
catastrophic forgetting of old CSI data.
