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

翻译：信道图是一种降维技术，它通过利用高维信道状态信息中的相似性关系，学习重建一个低维且物理可解释的无线环境地图。一类特定的信道图方法依赖于使用相异性度量计算的测量CSI数据点之间的伪距离。我们提出了几种技术来改进基于相异性度量的信道图性能。首先，我们解决了当将基于相异性度量的信道图应用于具有非凸低维结构的数据集时，欧几里得距离与测地线距离之间存在的差异问题。此外，我们通过将相异性建模为概率分布而非确定性量，将相异性不确定性纳入学习过程。我们的框架有助于以一致的方式组合多种相异性度量。此外，由于训练过程的改变，物理惯性导致的约束加速度等潜在空间动态特性也易于被考虑。我们在实测信道数据集上展示了所提方法在定位应用中实现的性能改进。