Channel charting creates a low-dimensional representation of the radio environment in a self-supervised manner using manifold learning. Preserving relative spatial distances in the latent space, channel charting is well suited to support user localization. While prior work on channel charting has mainly focused on two-dimensional scenarios, real-world environments are inherently three-dimensional. In this work, we investigate two distinct three-dimensional indoor localization scenarios using simulated, but realistic ray tracing-based datasets: a factory hall with a three-dimensional spatial distribution of datapoints, and a multistory building where each floor exhibits a two-dimensional datapoint distribution. For the first scenario, we apply the concept of augmented channel charting, which combines classical localization and channel charting, to a three-dimensional setting. For the second scenario, we introduce multistory channel charting, a two-stage approach consisting of floor classification via clustering followed by the training of a dedicated expert neural network for channel charting on each individual floor, thereby enhancing the channel charting performance. In addition, we propose a novel feature engineering method designed to extract sparse features from the beamspace channel state information that are suitable for localization.

翻译：信道图表化通过流形学习以自监督方式构建无线电环境的低维表示。该方法在潜在空间中保持相对空间距离，因此非常适用于支持用户定位。尽管先前关于信道图表化的研究主要集中于二维场景，但现实环境本质上是三维的。在本研究中，我们利用基于射线追踪的模拟但真实的数据集，探讨了两种不同的三维室内定位场景：一是具有三维数据点空间分布的工厂大厅，二是每层楼呈现二维数据点分布的多层建筑。针对第一种场景，我们将增强型信道图表化概念——结合了经典定位与信道图表化——应用于三维环境。针对第二种场景，我们提出了多层信道图表化方法，这是一种两阶段方案：首先通过聚类进行楼层分类，随后为每个独立楼层训练专用的专家神经网络进行信道图表化，从而提升信道图表化性能。此外，我们提出了一种新颖的特征工程方法，旨在从波束空间信道状态信息中提取适用于定位的稀疏特征。