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.

翻译：信道图谱是一种自监督学习技术，其目标是通过利用高维信道状态信息中的相似性关系，重建无线电环境的地图（称为信道图谱）。本文概述了信道图谱的处理流程与评估方法，并提出了一种考虑角度域信息的新型差异性度量，以及一种基于深度学习的新度量。此外，我们提出了一种融合差异性度量方法，使其在学习信道图谱时既能考虑信道测量的时间信息，也能兼顾信道状态信息中的相似性。通过将经典流形学习与深度学习流形学习应用于包含Sub-6GHz分布式大规模MIMO信道测量的数据集，我们证明所提出的度量优于先前提出的差异性度量方法。结果表明，即使在非视距条件下，新度量也能提升信道图谱的性能。