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Conference Papers Year : 2023

Channel Charting for Streaming CSI Data


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-maxsimilarity 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.
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Dates and versions

hal-04325927 , version 1 (06-12-2023)





Sueda Taner, M. Guillaud, Olav Tirkkonen, Christoph Studer. Channel Charting for Streaming CSI Data. Asilomar Conference on Signals, Systems, and Computers, Oct 2023, Pacific Grove, CA, United States. ⟨hal-04325927⟩
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