Feature Learning for Neural-Network-Based Positioning with Channel State Information
Feature Learning for Neural-Network-Based Positioning with Channel State Information
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DOI：
10.1109/ieeeconf53345.2021.9723124
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发表时间：
2021-10
期刊：
影响因子：
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通讯作者：
Emre Gonultacs;Sueda Taner;Howard Huang;Christoph Studer
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文献类型：
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作者：
Emre Gonultacs;Sueda Taner;Howard Huang;Christoph Studer
最近基于信道状态信息（CSI）的定位管道依赖于深度神经网络（DNN）来学习从估计的CSI到位置的映射。由于现实世界的通信收发器遭受硬件损伤，基于CSI的定位系统通常依赖于手工设计的功能。在本文中，我们提出了一种基于CSI的定位管道，它直接采用原始CSI测量并使用结构化DNN学习特征，以生成描述发射机位于预定义网格点的可能性的概率图。为了进一步提高移动用户设备的定位精度，我们建议融合学习的CSI特征的时间序列或概率图的时间序列。为了证明我们的方法的有效性，我们进行实验与现实世界的室内视线（LoS）和nonLoS信道测量。我们表明，CSI特征学习和时间序列融合可以将平均距离误差降低2.5倍。
Recent channel state information (CSI)-based positioning pipelines rely on deep neural networks (DNNs) in order to learn a mapping from estimated CSI to position. Since real-world communication transceivers suffer from hardware impairments, CSI-based positioning systems typically rely on features that are designed by hand. In this paper, we propose a CSI-based positioning pipeline that directly takes raw CSI measurements and learns features using a structured DNN in order to generate probability maps describing the likelihood of the transmitter being at pre-defined grid points. To further improve the positioning accuracy of moving user equipments, we propose to fuse a time-series of learned CSI features or a time-series of probability maps. To demonstrate the efficacy of our methods, we perform experiments with real-world indoor line-of-sight (LoS) and nonLoS channel measurements. We show that CSI feature learning and time-series fusion can reduce the mean distance error by up to 2.5× compared to the state-of-the-art.