Channel charting, an unsupervised learning method that learns a low-dimensional representation from channel information to preserve geometrical property of physical space of user equipments (UEs), has drawn many attentions from both academic and industrial communities, because it can facilitate many downstream tasks, such as indoor localization, UE handover, beam management, and so on. However, many previous works mainly focus on charting that only preserves local geometry and use raw channel information to learn the chart, which do not consider the global geometry and are often computationally intensive and very time-consuming. Therefore, in this paper, a novel signature based approach for global channel charting with ultra low complexity is proposed. By using an iterated-integral based method called signature transform, a compact feature map and a novel distance metric are proposed, which enable channel charting with ultra low complexity and preserving both local and global geometry. We demonstrate the efficacy of our method using synthetic and open-source real-field datasets.

翻译：信道制图作为一种无监督学习方法，通过从信道信息中学习低维表示来保留用户设备物理空间的几何特性，已引起学术界和工业界的广泛关注，因为它能够支持室内定位、用户设备切换、波束管理等诸多下游任务。然而，现有研究主要聚焦于仅保留局部几何特性的制图方法，且通常使用原始信道信息进行学习，这些方法既未考虑全局几何特性，又存在计算复杂度高、耗时长的问题。为此，本文提出了一种基于签名的新型超低复杂度全局信道制图方法。通过采用基于迭代积分的签名变换方法，我们构建了紧凑的特征图谱和新型距离度量，从而实现了同时保留局部与全局几何特性的超低复杂度信道制图。利用合成数据集和开源实测数据集，我们验证了所提方法的有效性。