Unsupervised Charting of Wireless Channels

Abstract

Future wireless communication systems will rely on large antenna arrays at the infrastructure base stations (BSs) to serve multiple users with high data rates in a single cell. We demonstrate that the availability of high-dimensional channel state information (CSI) acquired at such multi-antenna BSs enables one to learn a chart of the radio geometry, which captures the spatial geometry of the users so that points close in space are close in the channel chart, using no other information than wireless channels of users. Specifically, we propose a novel unsupervised framework that first extracts channel features from CSI which characterize large-scale fading effects of the channel, and then uses specialized dimensionality reduction tools to construct the channel chart. The channel chart can, for example, be used to perform (relative) user localization, predict cell hand-overs, or guide scheduling tasks, without accessing location information from global navigation satellite systems.