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

arXiv: https://arxiv.org/abs/2104.07963 · HTML: https://mlsanity.com/paper/2104.07963/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 17 references that are in the map.
- First posted on arXiv 16 Apr 2021.

## Builds on (most cited first, 4 of 4)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [srsLTE: An Open-Source Platform for LTE Evolution and Experimentation](https://mlsanity.com/paper/1602.04629/) (2016)
- [Recurrent Neural Networks For Accurate RSSI Indoor Localization](https://mlsanity.com/paper/1903.11703/) (2019)
- [Fingerprint-based Localization using Commercial LTE Signals: A Field-Trial Study](https://mlsanity.com/paper/1907.00594/) (2019)

## Built on by (most cited first, 1 of 1)

- [A Framework for CSI-Based Indoor Localization with 1D Convolutional Neural Networks](https://mlsanity.com/paper/2205.08068/) (2022)

## Abstract (arXiv)

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.