Channel Charting: Locating Users within the Radio Environment using Channel State Information
Christoph Studer, Sa\"id Medjkouh, Emre G\"on\"ulta\c{s}, Tom, Goldstein, Olav Tirkkonen

TL;DR
Channel charting is an unsupervised framework that maps radio geometry using channel state information, enabling user localization and network optimization without GPS or labeled data.
Contribution
The paper introduces a novel unsupervised method for creating radio environment maps from CSI, combining dimensionality reduction and neural networks.
Findings
Effective localization of users based on channel charts
Unsupervised approach eliminates need for labeled data
Potential applications in network management and user tracking
Abstract
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…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
