Dnn-Based Wireless Positioning in an Outdoor Environment

Jin Young Lee, Chahyeon Eom, Youngsu Kwak, Hong Goo Kang, Chungyong Lee

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

In this paper, we propose a deep learning based algorithm to estimate the position of an user by utilizing reference signal received power (RSRP) and the location of base stations. To obtain reliable results in a real communication environment, parameters were measured using commercially available base stations and mobile phones within a LTE network. Since the structure of the measured data changes in accordance with the number of connected base stations, it is necessary to work on data uniformity processing before running the deep learning network. Therefore, we extract only the case in which three base stations are connected, using it as a feature of deep learning network. The experimental results reveal that the performance of the proposed algorithm is much better than that of the conventional fingerprint method. The average distance error is reduced from 71.04 meters for the fingerprint-based method to 43.51 meters for the proposed deep learning-based method.

Original languageEnglish
Title of host publication2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages3799-3803
Number of pages5
ISBN (Print)9781538646588
DOIs
Publication statusPublished - 2018 Sep 10
Event2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Calgary, Canada
Duration: 2018 Apr 152018 Apr 20

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume2018-April
ISSN (Print)1520-6149

Conference

Conference2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018
CountryCanada
CityCalgary
Period18/4/1518/4/20

Fingerprint

Base stations
Mobile phones
Deep learning
Communication

All Science Journal Classification (ASJC) codes

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

Cite this

Lee, J. Y., Eom, C., Kwak, Y., Kang, H. G., & Lee, C. (2018). Dnn-Based Wireless Positioning in an Outdoor Environment. In 2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Proceedings (pp. 3799-3803). [8462098] (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings; Vol. 2018-April). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICASSP.2018.8462098
Lee, Jin Young ; Eom, Chahyeon ; Kwak, Youngsu ; Kang, Hong Goo ; Lee, Chungyong. / Dnn-Based Wireless Positioning in an Outdoor Environment. 2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2018. pp. 3799-3803 (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings).
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abstract = "In this paper, we propose a deep learning based algorithm to estimate the position of an user by utilizing reference signal received power (RSRP) and the location of base stations. To obtain reliable results in a real communication environment, parameters were measured using commercially available base stations and mobile phones within a LTE network. Since the structure of the measured data changes in accordance with the number of connected base stations, it is necessary to work on data uniformity processing before running the deep learning network. Therefore, we extract only the case in which three base stations are connected, using it as a feature of deep learning network. The experimental results reveal that the performance of the proposed algorithm is much better than that of the conventional fingerprint method. The average distance error is reduced from 71.04 meters for the fingerprint-based method to 43.51 meters for the proposed deep learning-based method.",
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Lee, JY, Eom, C, Kwak, Y, Kang, HG & Lee, C 2018, Dnn-Based Wireless Positioning in an Outdoor Environment. in 2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Proceedings., 8462098, ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, vol. 2018-April, Institute of Electrical and Electronics Engineers Inc., pp. 3799-3803, 2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018, Calgary, Canada, 18/4/15. https://doi.org/10.1109/ICASSP.2018.8462098

Dnn-Based Wireless Positioning in an Outdoor Environment. / Lee, Jin Young; Eom, Chahyeon; Kwak, Youngsu; Kang, Hong Goo; Lee, Chungyong.

2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2018. p. 3799-3803 8462098 (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings; Vol. 2018-April).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Lee JY, Eom C, Kwak Y, Kang HG, Lee C. Dnn-Based Wireless Positioning in an Outdoor Environment. In 2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2018. p. 3799-3803. 8462098. (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings). https://doi.org/10.1109/ICASSP.2018.8462098