Semi-supervised t-SNE for Millimeter-wave Wireless Localization
Abstract
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 tdistributed 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.
Cite
@article{arxiv.2111.13573,
title = {Semi-supervised t-SNE for Millimeter-wave Wireless Localization},
author = {Junquan Deng and Wei Shi and Jian Hu and Xianlong Jiao},
journal= {arXiv preprint arXiv:2111.13573},
year = {2021}
}
Comments
5 pages,6 figures, accepted to 7th International Conference on Computer and Communications