OpenCSI: An Open-Source Dataset for Indoor Localization Using CSI-Based Fingerprinting
This work provides a publicly available dataset for indoor localization research, addressing the effort-intensive data acquisition bottleneck in fingerprint-based methods.
The authors tackled the problem of indoor localization by automating radio map acquisition using a software-defined radio and a wheeled robot, and they open-sourced a dataset containing channel state information for LTE, achieving initial localization experiments with a convolutional neural network.
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.