Semi-Supervised Learning for Channel Charting-Aided IoT Localization in Millimeter Wave Networks
Semi-Supervised Learning for Channel Charting-Aided IoT Localization in Millimeter Wave Networks
复制标题
毫米波网络中信道图辅助物联网定位的半监督学习
DOI：
10.1109/globecom46510.2021.9685865
复制
发表时间：
2021
期刊：
影响因子：
--
通讯作者：
W. Saad
中科院分区：
文献类型：
--
作者：
Qianqian Zhang;W. Saad
提出了一种基于信道图(CC)的毫米波网络定位框架。特别地，提出了一种基于不同基站接收的多径信道状态信息(CSI)来估计无线用户设备(UE)的三维位置的卷积自动编码器模型。为了学习无线电几何映射并捕获每个UE的相对位置，以无监督的方式构建基于自动编码器的信道图，使得物理空间中的相邻UE在信道图中保持接近。接下来，将信道图模型扩展到半监督框架，将自动编码器分为编码器和解码器两个组件，并使用标记的CSI数据集和关联的位置信息分别对每个组件进行优化，以进一步提高定位精度。仿真结果表明，与现有的监督定位方法和传统的无监督CC方法相比，本文提出的CC辅助半监督定位方法具有更高的定位精度。
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.