Semi-supervised t-SNE for Millimeter-wave Wireless Localization
Published in The 7th International Conference on Computer and Communications (ICCC 2021), 2021
Recommended citation: Junquan Deng, Wei Shi, Jian Hu and Xianlong Jiao The 7th International Conference on Computer and Communications (ICCC 2021) . ICCC 2021. http://dengjunquan.github.io/files/ICCC2021.pdf
We consider the mobile localization problem in future millimeter-wave wireless networks with distributed Base Stations (BSs) based on multi-antenna channel state information (CSI). For this problem, we propose a Semi-supervised t-distributed Stochastic Neighbor Embedding (St-SNE) algorithm to directly embed the high-dimensional CSI samples into the 2D geographical map. We evaluate the performance of St-SNE in a simulated urban outdoor millimeter-wave radio access network. Our results show that St-SNE achieves a mean localization error of 6.8 m with only 5% of labeled CSI samples in a 200×200 m^2 area with a ray-tracing channel model. St-SNE does not require accurate synchronization among multiple BSs, and is promising for future large-scale millimeter-wave localization. Download paper here