Uncertainty-Aware Dimensionality Reduction for Channel Charting with Geodesic Loss

TL;DR

This paper enhances channel charting by addressing nonconvex data structures, incorporating uncertainty in dissimilarities, and enabling the combination of multiple metrics, leading to improved localization accuracy.

Contribution

It introduces a novel uncertainty-aware framework for dissimilarity metric-based channel charting that handles nonconvex structures and combines multiple metrics effectively.

Findings

Improved localization accuracy on measured datasets.

Effective handling of nonconvex low-dimensional structures.

Seamless integration of multiple dissimilarity metrics.

Abstract

Channel Charting is a dimensionality reduction technique that learns to reconstruct a low-dimensional, physically interpretable map of the radio environment by taking advantage of similarity relationships found in high-dimensional channel state information. One particular family of Channel Charting methods relies on pseudo-distances between measured CSI datapoints, computed using dissimilarity metrics. We suggest several techniques to improve the performance of dissimilarity metric-based Channel Charting. For one, we address an issue related to a discrepancy between Euclidean distances and geodesic distances that occurs when applying dissimilarity metric-based Channel Charting to datasets with nonconvex low-dimensional structure. Furthermore, we incorporate the uncertainty of dissimilarities into the learning process by modeling dissimilarities not as deterministic quantities, but as probability distributions. Our framework facilitates the combination of multiple dissimilarity metrics in a consistent manner. Additionally, latent space dynamics like constrained acceleration due to physical inertia are easily taken into account thanks to changes in the training procedure. We demonstrate the achieved performance improvements for localization applications on a measured channel dataset