Channel Charting aims to construct a map of the radio environment by leveraging similarity relationships found in high-dimensional channel state information. Although resulting channel charts usually accurately represent local neighborhood relationships, even under conditions with strong multipath propagation, they often fall short in capturing global geometric features. On the other hand, classical model-based localization methods, such as triangulation and multilateration, can easily localize signal sources in the global coordinate frame. However, these methods rely heavily on the assumption of line-of-sight channels and distributed antenna deployments. Based on measured data, we compare classical source localization techniques to channel charts with respect to localization performance. We suggest and evaluate methods to enhance Channel Charting with model-based localization approaches: One approach involves using information derived from classical localization methods to map channel chart locations to physical positions after conventional training of the forward charting function. Foremost, though, we suggest to incorporate information from model-based approaches during the training of the forward charting function in what we call "augmented Channel Charting". We demonstrate that Channel Charting can outperform classical localization methods on the considered dataset.

翻译：信道图构建旨在利用高维信道状态信息中的相似性关系构建无线电环境地图。尽管生成的信道图通常能准确反映局部邻域关系，即便在强多径传播条件下，但其在捕捉全局几何特征方面仍显不足。另一方面，基于经典模型定位方法（如三角定位法和多点定位法）可轻松在全局坐标框架中定位信号源，但这些方法严重依赖视距信道和分布式天线部署假设。本文基于实测数据，从定位性能角度对比了经典源定位技术与信道图构建方法。我们提出并评估了两种将基于模型的定位方法融入信道图构建的技术：第一种方法是在前向映射函数常规训练完成后，利用经典定位方法推导的信息将信道图位置映射至物理坐标。但更重要的是，我们提出在"增强型信道图构建"框架下，于前向映射函数训练过程中融入基于模型方法的信息。实验证明，在所选数据集上，信道图构建可优于经典定位方法。