We propose channel charting (CC), a novel framework in which a multi-antenna
network element learns a chart of the radio geometry in its surrounding area.
The channel chart captures the local spatial geometry of the area so that
points that are close in space will also be close in the channel chart and vice
versa. CC works in a fully unsupervised manner, i.e., learning is only based on
channel state information (CSI) that is passively collected at a single point
in space, but from multiple transmit locations in the area over time. The
method then extracts channel features that characterize large-scale fading
properties of the wireless channel. Finally, the channel charts are generated
with tools from dimensionality reduction, manifold learning, and deep neural
networks. The network element performing CC may be, for example, a
multi-antenna base-station in a cellular system and the charted area in the
served cell. Logical relationships related to the position and movement of a
transmitter, e.g., a user equipment (UE), in the cell can then be directly
deduced from comparing measured radio channel characteristics to the channel
chart. The unsupervised nature of CC enables a range of new applications in UE
localization, network planning, user scheduling, multipoint connectivity,
hand-over, cell search, user grouping, and other cognitive tasks that rely on
CSI and UE movement relative to the base-station, without the need of
information from global navigation satellite systems.

本文提出了一种基于信道状态信息无监督学习的多天线网络元素的信道绘图框架，在对无线电链路进行大规模衰落特性特征提取后，通过降维和深度神经网络生成信道图，从而实现对用户设备 (UE) 在服务区内的位置和移动所涉及的逻辑关系的直接推断，为 UE 的本地化、网络规划、用户调度、多点连接、移动手段等认知任务提供了一种新的无需全球导航卫星系统信息的解决方案。