论文标题
基于三胞胎的无线通道图表：体系结构和实验
Triplet-Based Wireless Channel Charting: Architecture and Experiments
论文作者
论文摘要
频道图表是一种数据驱动的基带处理技术，旨在将自我监督的机器学习技术应用于渠道状态信息（CSI），目的是降低数据的尺寸并提取控制其分布的基本参数。我们介绍了一种基于样本三重的新型渠道图表方法。拟议的算法在其各自的采集时间的接近度中学习了CSI样本之间有意义的相似性度量，并同时降低了维度。我们对从商业大规模MIMO系统获得的数据进行了广泛的实验验证；特别是，我们评估所获得的通道图与用户位置信息相似的程度，尽管没有通过任何地理数据监督。最后，我们建议和评估通道图表过程中的变化，包括部分监督的情况，其中一些标签可用于数据集的一部分。
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.
