Channel charting is an emerging technology that enables self-supervised pseudo-localization of user equipments by performing dimensionality reduction on large channel-state information (CSI) databases that are passively collected at infrastructure base stations or access points. In this paper, we introduce a new dimensionality reduction method specifically designed for channel charting using a novel split triplet loss, which utilizes physical information available during the CSI acquisition process. In addition, we propose a novel regularizer that exploits the physical concept of inertia, which significantly improves the quality of the learned channel charts. We provide an experimental verification of our methods using synthetic and real-world measured CSI datasets, and we demonstrate that our methods are able to outperform the state-of-the-art in channel charting based on the triplet loss.

翻译：频道图案是一种新兴技术,它通过在基础设施基站或接入点被动收集大型频道状态信息数据库(CSI),使用户设备实现自我监督的伪本地化。 在本文中,我们引入了一种新的维度减少方法,专门设计用于使用新颖的三重分解损失的频道图案,该方法利用CSI获取过程中可获得的实物信息。此外,我们提议了一种新颖的固定装置,利用惯性物理概念,大大提高了所学通道图的质量。我们用合成和真实世界测量的CSI数据集对我们的方法进行了实验性核查,我们证明我们的方法能够超越基于三重分解损失的频道图案。