  Channel charting, an unsupervised learning method that learns a
low-dimensional representation from channel information to preserve geometrical
property of physical space of user equipments (UEs), has drawn many attentions
from both academic and industrial communities, because it can facilitate many
downstream tasks, such as indoor localization, UE handover, beam management,
and so on. However, many previous works mainly focus on charting that only
preserves local geometry and use raw channel information to learn the chart,
which do not consider the global geometry and are often computationally
intensive and very time-consuming. Therefore, in this paper, a novel signature
based approach for global channel charting with ultra low complexity is
proposed. By using an iterated-integral based method called signature
transform, a compact feature map and a novel distance metric are proposed,
which enable channel charting with ultra low complexity and preserving both
local and global geometry. We demonstrate the efficacy of our method using
synthetic and open-source real-field datasets.
