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.

翻译：信道图表（Channel Charting, CC）方法在基础设施基站侧对信道状态信息（Channel State Information, CSI）数据进行降维，旨在提取每个用户的伪位置信息。CC的自监督特性使其能够在无需真实位置信息的前提下，执行依赖于用户位置的预测任务。本文聚焦于实际场景中的流式CSI数据场景——即CSI被持续估计的情况。为解决存储限制问题，我们提出了一种新型流式CC架构，该架构维护一个小型核心CSI数据集，并基于此学习信道图表。核心CSI数据集的筛选采用最小-最大相似度准则实现。通过实测CSI数据的数值验证表明，本方法仅需少量CSI存储空间，即可接近完整CSI数据集的精度，同时避免对旧CSI数据的灾难性遗忘。