A Localization Method with Insufficient RSS samples using Neural Network

Sunghoon Jung; Chaehun Im; Chahyeon Eom; Chungyong Lee

doi:10.1109/ICAIIC48513.2020.9065087

A Localization Method with Insufficient RSS samples using Neural Network

Sunghoon Jung, Chaehun Im, Chahyeon Eom, Chungyong Lee

Department of Electrical and Electronic Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

3 Citations (Scopus)

Abstract

In this paper, we propose a localization method with insufficient received signal strength (RSS) samples using the neural network. The proposed method estimates the position of the user equipment (UE) with the position information of the base stations and collected RSS samples. The proposed method estimates the position of the UE more accurately with only a few RSS samples which are insufficient to correct the RSS value by the conventional method. Also, all the localization processes are integrated into a single neural network, unlike the conventional method needs two-step processes. The proposed method is superior to the conventional method in terms of root mean squared error and circular error probability although using insufficient RSS samples. The average of the localization accuracy is improved by 38% compared with the conventional method.

Original language	English
Title of host publication	2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	272-275
Number of pages	4
ISBN (Electronic)	9781728149851
DOIs	https://doi.org/10.1109/ICAIIC48513.2020.9065087
Publication status	Published - 2020 Feb
Event	2nd International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020 - Fukuoka, Japan Duration: 2020 Feb 19 → 2020 Feb 21

Publication series

Name	2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020

Conference

Conference	2nd International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020
Country/Territory	Japan
City	Fukuoka
Period	20/2/19 → 20/2/21

Bibliographical note

Funding Information:
This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government( MSIT) (NRF-2019R1A2C1010950)

Publisher Copyright:
© 2020 IEEE.

All Science Journal Classification (ASJC) codes

Information Systems and Management
Artificial Intelligence
Computer Networks and Communications
Computer Vision and Pattern Recognition
Information Systems
Signal Processing

Access to Document

10.1109/ICAIIC48513.2020.9065087

Cite this

Jung, S., Im, C., Eom, C., & Lee, C. (2020). A Localization Method with Insufficient RSS samples using Neural Network. In 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020 (pp. 272-275). Article 9065087 (2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICAIIC48513.2020.9065087

Jung, Sunghoon ; Im, Chaehun ; Eom, Chahyeon et al. / A Localization Method with Insufficient RSS samples using Neural Network. 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020. Institute of Electrical and Electronics Engineers Inc., 2020. pp. 272-275 (2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020).

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abstract = "In this paper, we propose a localization method with insufficient received signal strength (RSS) samples using the neural network. The proposed method estimates the position of the user equipment (UE) with the position information of the base stations and collected RSS samples. The proposed method estimates the position of the UE more accurately with only a few RSS samples which are insufficient to correct the RSS value by the conventional method. Also, all the localization processes are integrated into a single neural network, unlike the conventional method needs two-step processes. The proposed method is superior to the conventional method in terms of root mean squared error and circular error probability although using insufficient RSS samples. The average of the localization accuracy is improved by 38% compared with the conventional method.",

author = "Sunghoon Jung and Chaehun Im and Chahyeon Eom and Chungyong Lee",

note = "Funding Information: This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government( MSIT) (NRF-2019R1A2C1010950) Publisher Copyright: {\textcopyright} 2020 IEEE.; 2nd International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020 ; Conference date: 19-02-2020 Through 21-02-2020",

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series = "2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020",

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Jung, S, Im, C, Eom, C & Lee, C 2020, A Localization Method with Insufficient RSS samples using Neural Network. in 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020., 9065087, 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Institute of Electrical and Electronics Engineers Inc., pp. 272-275, 2nd International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, 20/2/19. https://doi.org/10.1109/ICAIIC48513.2020.9065087

A Localization Method with Insufficient RSS samples using Neural Network. / Jung, Sunghoon; Im, Chaehun; Eom, Chahyeon et al.
2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020. Institute of Electrical and Electronics Engineers Inc., 2020. p. 272-275 9065087 (2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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T1 - A Localization Method with Insufficient RSS samples using Neural Network

AU - Jung, Sunghoon

AU - Im, Chaehun

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N2 - In this paper, we propose a localization method with insufficient received signal strength (RSS) samples using the neural network. The proposed method estimates the position of the user equipment (UE) with the position information of the base stations and collected RSS samples. The proposed method estimates the position of the UE more accurately with only a few RSS samples which are insufficient to correct the RSS value by the conventional method. Also, all the localization processes are integrated into a single neural network, unlike the conventional method needs two-step processes. The proposed method is superior to the conventional method in terms of root mean squared error and circular error probability although using insufficient RSS samples. The average of the localization accuracy is improved by 38% compared with the conventional method.

AB - In this paper, we propose a localization method with insufficient received signal strength (RSS) samples using the neural network. The proposed method estimates the position of the user equipment (UE) with the position information of the base stations and collected RSS samples. The proposed method estimates the position of the UE more accurately with only a few RSS samples which are insufficient to correct the RSS value by the conventional method. Also, all the localization processes are integrated into a single neural network, unlike the conventional method needs two-step processes. The proposed method is superior to the conventional method in terms of root mean squared error and circular error probability although using insufficient RSS samples. The average of the localization accuracy is improved by 38% compared with the conventional method.

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Jung S, Im C, Eom C, Lee C. A Localization Method with Insufficient RSS samples using Neural Network. In 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020. Institute of Electrical and Electronics Engineers Inc. 2020. p. 272-275. 9065087. (2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020). doi: 10.1109/ICAIIC48513.2020.9065087

A Localization Method with Insufficient RSS samples using Neural Network

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