OpenCSI: An Open-Source Dataset for Indoor Localization Using CSI-Based Fingerprinting

TL;DR

This paper introduces OpenCSI, an open-source dataset for indoor localization using CSI fingerprinting, created with automated tools and tested with neural networks, addressing the challenge of effort-intensive radio map collection.

Contribution

It presents the first publicly available CSI-based radio map for LTE, generated via automation, and demonstrates initial localization experiments with CNNs.

Findings

First open-source CSI radio map for LTE

Automated radio map acquisition method

Initial CNN-based localization results

Abstract

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.