  Channel charting is a data-driven baseband processing technique consisting in
applying self-supervised machine learning techniques to channel state
information (CSI), with the objective of reducing the dimension of the data and
extracting the fundamental parameters governing its distribution. We introduce
a novel channel charting approach based on triplets of samples. The proposed
algorithm learns a meaningful similarity metric between CSI samples on the
basis of proximity in their respective acquisition times, and simultaneously
performs dimensionality reduction. We present an extensive experimental
validation of the proposed approach on data obtained from a commercial Massive
MIMO system; in particular, we evaluate to which extent the obtained channel
chart is similar to the user location information, although it is not
supervised by any geographical data. Finally, we propose and evaluate
variations in the channel charting process, including the partially supervised
case where some labels are available for part of the dataset.
