High-Precision Indoor Fingerprint Localization Based on Graph Neural Network
Xiangxu Meng, Wei Li, Zheng Zhao, Yinan Cai, Guoqing Liu · 2023
Thanks to the rapid development of machine learning and deep learning, the fingerprint localization community has achieved high localization performance through advanced models. However, current approaches mainly focus on machine learning or pure convolutions algorithms for example k-Nearest Neighbor (KNN) and Convolutional Neural Network (CNN). Despite their success, their localization ability is mainly derived from the development of model learning ability without in-depth study of the physical meaning of Channel State Information (CSI), such as the varying degrees of correlation and interference between subcarriers of antennas located at different locations in a wireless communication system. In particular, we develop a novel graph neural network (GNN)-based method to embed subcarriers from different antennas at different positions of the CSI as nodes and connect them to K-nearest neighbours (KNNs) to obtain a cohesive graph structure. Furthermore, we introduce a dilated KNN to perform graph-level learning using graph convolution, effectively modeling the correlation and interference between subcarriers. Finally, to prevent the degradation of positioning performance caused by reduced node feature diversity, we introduce a feedforward neural network (FFN) module for node feature transformation. Experimental results on real datasets indicate that our method achieves an average localization error of 0.17m, which is almost 39.3% better than the existing optimal baseline.