一种基于签名变换的超低复杂度全局信道图谱方法
A Signature Based Approach Towards Global Channel Charting with Ultra Low Complexity
AI总结：
本文提出基于签名变换的信道图谱方法，通过紧凑特征映射和新颖距离度量，以超低复杂度同时保持局部与全局几何结构，并在合成与实测数据集上验证了有效性。
摘要：
信道图谱是一种无监督学习方法，它从信道信息中学习低维表示，以保持用户设备（UE）物理空间的几何特性，近年来引起了学术界和工业界的广泛关注，因为它可以促进许多下游任务，如室内定位、UE切换、波束管理等。然而，许多先前的工作主要关注仅保持局部几何结构的图谱构建，并使用原始信道信息来学习图谱，这些方法没有考虑全局几何结构，并且通常计算密集且非常耗时。因此，本文提出了一种新颖的基于签名变换的超低复杂度全局信道图谱方法。通过使用一种基于迭代积分的方法，即签名变换，提出了一种紧凑的特征映射和一种新颖的距离度量，使得信道图谱能够以超低复杂度构建，并同时保持局部和全局几何结构。我们使用合成数据集和开源实测数据集验证了所提方法的有效性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
