# Improving Channel Charting with Representation-Constrained Autoencoders

## TL;DR

This paper enhances channel charting by integrating side information into autoencoders, improving the spatial accuracy of user equipment positioning solely from channel-state information without extensive measurements.

## Contribution

It introduces representation-constrained autoencoders that incorporate side information to better preserve the global geometry of channel charts for positioning.

## Key findings

- Representation constraints improve channel chart quality
- Autoencoders recover global geometry effectively
- Positioning accuracy is enhanced without GPS or extensive data

## Abstract

Channel charting (CC) has been proposed recently to enable logical positioning of user equipments (UEs) in the neighborhood of a multi-antenna base-station solely from channel-state information (CSI). CC relies on dimensionality reduction of high-dimensional CSI features in order to construct a channel chart that captures spatial and radio geometries so that UEs close in space are close in the channel chart. In this paper, we demonstrate that autoencoder (AE)-based CC can be augmented with side information that is obtained during the CSI acquisition process. More specifically, we propose to include pairwise representation constraints into AEs with the goal of improving the quality of the learned channel charts. We show that such representation-constrained AEs recover the global geometry of the learned channel charts, which enables CC to perform approximate positioning without global navigation satellite systems or supervised learning methods that rely on extensive and expensive measurement campaigns.