Channel Charting for Streaming CSI Data

Sueda Taner, Maxime Guillaud, Olav Tirkkonen, Christoph Studer
Computer Science, Information Theory, Information Theory (cs.IT), Signal Processing (eess.SP)
2023-12-07 00:00:00
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.
PDF: Channel Charting for Streaming CSI Data.pdf
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