Graph Convolutional Network Empowered Indoor Localization Method via Aggregating MIMO CSI
Globecom, pp. 6481–6486
Abstract
With the explosive growth of advanced wireless technologies and computing device platforms, mobile sensing has gained huge attention. Indoor localization is actually considered as one of most valuable techniques in the field of contactless sensing. In this paper, we propose a novel graph convolutional network (GCN) empowered indoor localization method, which aggregates channel state information (CSI) features extracted from multiple multiple-input multiple-output (MIMO) links. CSI features from multiple antennas are basically converted into graph nodes in order to adopt GCN classification model. At the same time, graph attention mechanism is introduced to study and transfer spatial and frequency of CSI features. Eventually, output of graph is mapped with multiple measurement points through prediction network to provide final estimate position. 5GHz commercial Wi-Fi equipment is respectively utilized for data collection and experimental evaluation in two representative indoor scenarios. Experimental result shows that the proposed method has better performance in robust localization compared to other state-of-the-art deep learning methods.
Authors 5
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Affiliation as printed
School of Electronic Science and Engineering, Southeast University,Nanjing,China
School of Electronic Science and Engineering, Southeast University, Nanjing, China
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Affiliation as printed
School of Electronic Science and Engineering, Southeast University,Nanjing,China
School of Electronic Science and Engineering, Southeast University, Nanjing, China
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Nanjing University of Posts and Telecommunications
Affiliation as printed
College of Telecommunications and Information Engineering, NJUPT,Nanjing,China
College of Telecommunications and Information Engineering, NJUPT, Nanjing, China
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Nanjing University of Posts and Telecommunications
Affiliation as printed
College of Telecommunications and Information Engineering, NJUPT,Nanjing,China
College of Telecommunications and Information Engineering, NJUPT, Nanjing, China
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Affiliation as printed
Institute for Communication Technologies and Embedded Systems, RWTH Aachen University,Aachen,Germany
Institute for Communication Technologies and Embedded Systems, RWTH Aachen University, Aachen, Germany
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