  Channel charting is a self-supervised learning technique whose objective is
to reconstruct a map of the radio environment, called channel chart, by taking
advantage of similarity relationships in high-dimensional channel state
information. We provide an overview of processing steps and evaluation methods
for channel charting and propose a novel dissimilarity metric that takes into
account angular-domain information as well as a novel deep learning-based
metric. Furthermore, we suggest a method to fuse dissimilarity metrics such
that both the time at which channels were measured as well as similarities in
channel state information can be taken into consideration while learning a
channel chart. By applying both classical and deep learning-based manifold
learning to a dataset containing sub-6GHz distributed massive MIMO channel
measurements, we show that our metrics outperform previously proposed
dissimilarity measures. The results indicate that the new metrics improve
channel charting performance, even under non-line-of-sight conditions.
