C-GCN: A Flexible CSI Phase Feature Extraction Network for Error Suppression in Indoor Positioning
Channel state information (CSI) provides a fine-grained description of the signal propagation process, which has attracted extensive attention in the field of indoor positioning. However, considering the influence of environment and hardware, the phase of CSI is distorted in most cases. It is diffic...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-07-01
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| Series: | Entropy |
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| Online Access: | https://www.mdpi.com/1099-4300/23/8/1004 |
| _version_ | 1797523898133118976 |
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| author | Wen Liu Qianqian Cheng Zhongliang Deng Mingjie Jia |
| author_facet | Wen Liu Qianqian Cheng Zhongliang Deng Mingjie Jia |
| author_sort | Wen Liu |
| collection | DOAJ |
| description | Channel state information (CSI) provides a fine-grained description of the signal propagation process, which has attracted extensive attention in the field of indoor positioning. However, considering the influence of environment and hardware, the phase of CSI is distorted in most cases. It is difficult to extract effective location features in multiple scenes only through the determined artificial experience model. Graph neural network has performed well in many fields in recent years, but there is still a lot of room to explore in the field of indoor positioning. In this paper, a phase feature extraction network based on multi-dimensional correlation is proposed, named Cooperation-Graph Convolution Network (C-GCN). The purpose of C-GCN is to extract new features of multiple correlation and to mine the relationship between antenna and subcarrier as much as possible. C-GCN is composed of convolution layer and graph convolution layer. In the graph convolution layer, C-GCN regards each subcarrier of each antenna as a node in the graph network, constructs the connection by the correlation between the antenna and the subcarrier, and aggregates the node vectors by graph convolution. In the convolution layer, there is a natural corresponding structure between data packets, C-GCN extracts the fluctuation with convolution in Euclidean space. C-GCN combines these two layers, and applies end-to-end supervised training to obtain effective features. Extensive experiments are conducted in typical indoor environments to verify the superior performance of C-GCN in restraining error tailing. The average positioning error of C-GCN is 1.29 m in comprehensive office and 1.71 m in garage. Combined with the amplitude feature, the average positioning error is 0.99 m in comprehensive office and 1.14 m in garage. |
| first_indexed | 2024-03-10T08:49:39Z |
| format | Article |
| id | doaj.art-512009ee2fe04efb87385d5fc16e2046 |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-03-10T08:49:39Z |
| publishDate | 2021-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj.art-512009ee2fe04efb87385d5fc16e20462023-11-22T07:34:53ZengMDPI AGEntropy1099-43002021-07-01238100410.3390/e23081004C-GCN: A Flexible CSI Phase Feature Extraction Network for Error Suppression in Indoor PositioningWen Liu0Qianqian Cheng1Zhongliang Deng2Mingjie Jia3School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, ChinaSchool of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, ChinaSchool of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, ChinaSchool of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, ChinaChannel state information (CSI) provides a fine-grained description of the signal propagation process, which has attracted extensive attention in the field of indoor positioning. However, considering the influence of environment and hardware, the phase of CSI is distorted in most cases. It is difficult to extract effective location features in multiple scenes only through the determined artificial experience model. Graph neural network has performed well in many fields in recent years, but there is still a lot of room to explore in the field of indoor positioning. In this paper, a phase feature extraction network based on multi-dimensional correlation is proposed, named Cooperation-Graph Convolution Network (C-GCN). The purpose of C-GCN is to extract new features of multiple correlation and to mine the relationship between antenna and subcarrier as much as possible. C-GCN is composed of convolution layer and graph convolution layer. In the graph convolution layer, C-GCN regards each subcarrier of each antenna as a node in the graph network, constructs the connection by the correlation between the antenna and the subcarrier, and aggregates the node vectors by graph convolution. In the convolution layer, there is a natural corresponding structure between data packets, C-GCN extracts the fluctuation with convolution in Euclidean space. C-GCN combines these two layers, and applies end-to-end supervised training to obtain effective features. Extensive experiments are conducted in typical indoor environments to verify the superior performance of C-GCN in restraining error tailing. The average positioning error of C-GCN is 1.29 m in comprehensive office and 1.71 m in garage. Combined with the amplitude feature, the average positioning error is 0.99 m in comprehensive office and 1.14 m in garage.https://www.mdpi.com/1099-4300/23/8/1004channel state informationphasefeature extractionfingerprint localization |
| spellingShingle | Wen Liu Qianqian Cheng Zhongliang Deng Mingjie Jia C-GCN: A Flexible CSI Phase Feature Extraction Network for Error Suppression in Indoor Positioning Entropy channel state information phase feature extraction fingerprint localization |
| title | C-GCN: A Flexible CSI Phase Feature Extraction Network for Error Suppression in Indoor Positioning |
| title_full | C-GCN: A Flexible CSI Phase Feature Extraction Network for Error Suppression in Indoor Positioning |
| title_fullStr | C-GCN: A Flexible CSI Phase Feature Extraction Network for Error Suppression in Indoor Positioning |
| title_full_unstemmed | C-GCN: A Flexible CSI Phase Feature Extraction Network for Error Suppression in Indoor Positioning |
| title_short | C-GCN: A Flexible CSI Phase Feature Extraction Network for Error Suppression in Indoor Positioning |
| title_sort | c gcn a flexible csi phase feature extraction network for error suppression in indoor positioning |
| topic | channel state information phase feature extraction fingerprint localization |
| url | https://www.mdpi.com/1099-4300/23/8/1004 |
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