  Channel charting is an unsupervised learning task whose objective is to
encode channels so that the obtained representation reflects the relative
spatial locations of the corresponding users. It has many potential
applications, ranging from user scheduling to proactive handover. In this
paper, a channel charting method is proposed, based on a distance measure
specifically designed to reduce the effect of small scale fading, which is an
irrelevant phenomenon with respect to the channel charting task. A nonlinear
dimensionality reduction technique aimed at preserving local distances (Isomap)
is then applied to actually get the channel representation. The approach is
empirically validated on realistic synthetic \new{multipath} MIMO channels,
achieving better results than previously proposed approaches, at a lower cost.
