Channel charting is a data-driven baseband processing technique consisting in applying self-supervised machine learning techniques to channel state information (CSI), with the objective of reducing the dimension of the data and extracting the fundamental parameters governing its distribution. We introduce a novel channel charting approach based on triplets of samples. The proposed algorithm learns a meaningful similarity metric between CSI samples on the basis of proximity in their respective acquisition times, and simultaneously performs dimensionality reduction. We present an extensive experimental validation of the proposed approach on data obtained from a commercial Massive MIMO system; in particular, we evaluate to which extent the obtained channel chart is similar to the user location information, although it is not supervised by any geographical data. Finally, we propose and evaluate variations in the channel charting process, including the partially supervised case where some labels are available for part of the dataset.

中文翻译：

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基于三元组的无线信道图表：架构和实验

信道图是一种数据驱动的基带处理技术，包括将自监督机器学习技术应用于信道状态信息 (CSI)，目的是减少数据的维度并提取控制其分布的基本参数。我们介绍了一种基于三元组样本的新型通道图表方法。所提出的算法根据各自采集时间的接近度学习 CSI 样本之间有意义的相似性度量，并同时执行降维。我们对从商业大规模 MIMO 系统获得的数据提出的方法进行了广泛的实验验证；特别是，我们评估获得的频道图表在多大程度上与用户位置信息相似，尽管它不受任何地理数据的监督。最后，我们提出并评估通道制图过程中的变化，包括部分监督的情况，其中一些标签可用于部分数据集。