Many applications require accurate indoor localization. Fingerprint-based localization methods propose a solution to this problem, but rely on a radio map that is effort-intensive to acquire. We automate the radio map acquisition phase using a software-defined radio (SDR) and a wheeled robot. Furthermore, we open-source a radio map acquired with our automated tool for a 3GPP Long-Term Evolution (LTE) wireless link. To the best of our knowledge, this is the first publicly available radio map containing channel state information (CSI). Finally, we describe first localization experiments on this radio map using a convolutional neural network to regress for location coordinates.
@article{arxiv.2104.07963,
title = {OpenCSI: An Open-Source Dataset for Indoor Localization Using CSI-Based Fingerprinting},
author = {Arthur Gassner and Claudiu Musat and Alexandru Rusu and Andreas Burg},
journal= {arXiv preprint arXiv:2104.07963},
year = {2022}
}