In this paper, a novel framework is proposed for channel charting (CC)-aided localization in millimeter wave networks. In particular, a convolutional autoencoder model is proposed to estimate the three-dimensional location of wireless user equipment (UE), based on multipath channel state information (CSI), received by different base stations. In order to learn the radio-geometry map and capture the relative position of each UE, an autoencoder-based channel chart is constructed in an unsupervised manner, such that neighboring UEs in the physical space will remain close in the channel chart. Next, the channel charting model is extended to a semi-supervised framework, where the autoencoder is divided into two components: an encoder and a decoder, and each component is optimized individually, using the labeled CSI dataset with associated location information, to further improve positioning accuracy. Simulation results show that the proposed CC-aided semi-supervised localization yields a higher accuracy, compared with existing supervised positioning and conventional unsupervised CC approaches.
翻译:在本文中,为在毫米波网络中绘制频道图(CC)辅助本地化提出了一个新的框架。特别是,提议了一个卷轴自动编码模型,以估计不同基站收到的基于多路径频道状态信息的无线用户设备(UE)的三维位置。为了学习无线电地球测量图并捕捉每个UE的相对位置,以不受监督的方式构建了一个基于自动编码的频道图,这样物理空间的邻近UE将仍然接近频道图。接下来,频道图表模型将扩展至一个半监督框架,将自动编码器分为两个部分:一个编码器和一个解码器,每个部分单独优化,使用带有相关位置信息的标签的CSI数据集,进一步提高定位准确性。模拟结果显示,与现有的监督定位和常规的未监督的CC方法相比,拟议的CC辅助半监控定位图将产生更高的准确性。