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C-GCN: A Flexible CSI Phase Feature Extraction Network for Error Suppression in Indoor Positioning
2021
Entropy
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
doi:10.3390/e23081004
fatcat:dgwgwai4lrg7rbzpn4bv6vajs4