[KSCI] Korea Science Citation Index Service

http://dx.doi.org/10.11003/JPNT.2020.9.3.169

Analysis of Outdoor Positioning Results using Deep Learning Based LTE CSI-RS Data

Jeon, Juil (Electronics and Telecommunications Research Institute)
Ji, Myungin (Electronics and Telecommunications Research Institute)
Cho, Youngsu (Electronics and Telecommunications Research Institute)

Publication Information

Journal of Positioning, Navigation, and Timing / v.9, no.3, 2020 , pp. 169-173 More about this Journal

Abstract

Location-based services are used as core services in various fields. In particular, in the field of public services such as emergency rescue, accurate location estimation technology is very important. Recently, the technology of tracking the location of self-isolation subjects for COVID-19 has become a major issue. Therefore, location estimation technology using personal smart devices is being studied in various ways, and the most widely used method is to use GPS. Other representative methods are using Wi-Fi, Pedestrian Dead Reckoning (PDR), Bluetooth Low Energy (BLE) beacons, and LTE signals. In this paper, we introduced a positioning technology using deep learning based on LTE Channel State Information-Reference Signal (CSI-RS) data, and confirmed the possibility through an outdoor location estimation experiment using a commercial LTE signal.

Keywords

LBS; CSI-RS; deep learning;

Citations & Related Records

Reference

1	Ericsson Whire Paper, Positioning with LTE, 284 23-3155 Uen [Internet], cited September 2011, available from: https://www.sharetechnote.com/Docs/WP-LTEpositioning.pdf
2	Gu, Y., Lo, A., & Niemegeers, L. 2009, A survey of indoor positioning systems for wireless personal networks, IEEE Commun. Surv. Tutorials, 11, 13-32. https://doi.org/10.1109/SURV.2009.090103 DOI
3	Pecoraro, G., Di Domenico, S., Cianca, E., & De Sanctis, M. 2018, CSI-based fingerprinting for indoor localization using LTE Signals, EURASIP Journal on Advances in Signal Processing, 49. https://doi.org/10.1186/s13634-018-0563-7
4	Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., et al. 2014, Going Deeper with Convolutions, arXiv:1409.4842v1 [cs. CV] 17 Sep 2014
5	Vo, Q. S. & De, P. 2016, A survey of fingerprint based outdoor localization, IEEE Communications Surveys & Tutorials, 18, 491-506. https://doi.org/10.1109/COMST.2015.2448632 DOI
6	Wang, X., Gao, L., & Mao, S. 2016, CSI phase fingerprinting for indoor localization with a deep learning approach, IEEE Internet Things J., 3, 1113-1123. https://doi.org/10.1109/JIOT.2016.2558659 DOI
7	Wang, X., Gao, L., Mao, S., & Pandey, S. 2017, CSI-Based Fingerprinting for Indoor Localization: A Deep Learning Approach, IEEE Trans. Veh. Technol., 66, 763-776. https://doi.org/10.1109/TVT.2016.2545523 DOI
8	Wu, K., Xiao, J., Yi, Y., Chen, D., Luo, X., et al. 2013, CSIBased Indoor Localization, IEEE Transactions on Parallel and Distributed Systems, 24, 1300-1309. https://doi.org/10.1109/TPDS.2012.214 DOI
9	del Peral-Rosado, J. A., Raulefs, R., Lopez-Salcedo, J. A., & Seco-Granados, G. 2018, Survey of cellular mobile radio localization methods: from 1G to 5G, IEEE Communications Surveys & Tutorials, 20, 1124-1148. https://doi.org/10.1109/COMST.2017.2785181 DOI