Angle-Delay Profile-Based and Timestamp-Aided Dissimilarity Metrics for Channel Charting

TL;DR

This paper introduces novel dissimilarity metrics for channel charting that incorporate angular and timestamp information, improving the accuracy of radio environment mapping using deep learning and manifold learning techniques.

Contribution

It proposes new angular-delay profile-based and timestamp-aided dissimilarity metrics, along with a fusion method, enhancing channel charting performance over existing measures.

Findings

Metrics outperform previous dissimilarity measures

Improved channel charting under non-line-of-sight conditions

Deep learning-based metrics enhance mapping accuracy

Abstract

Channel charting is a self-supervised learning technique whose objective is to reconstruct a map of the radio environment, called channel chart, by taking advantage of similarity relationships in high-dimensional channel state information. We provide an overview of processing steps and evaluation methods for channel charting and propose a novel dissimilarity metric that takes into account angular-domain information as well as a novel deep learning-based metric. Furthermore, we suggest a method to fuse dissimilarity metrics such that both the time at which channels were measured as well as similarities in channel state information can be taken into consideration while learning a channel chart. By applying both classical and deep learning-based manifold learning to a dataset containing sub-6GHz distributed massive MIMO channel measurements, we show that our metrics outperform previously proposed dissimilarity measures. The results indicate that the new metrics improve channel charting performance, even under non-line-of-sight conditions.