Deep Learning Based CoMP Transmission Method Using Vehicle Position Information for Taxi Radio Systems

Kazuki Kojima; Yukiko Shimbo; Hirofumi Suganuma; Fumiaki Maehara

doi:10.1109/ICAIIC48513.2020.9065193

Deep Learning Based CoMP Transmission Method Using Vehicle Position Information for Taxi Radio Systems

Kazuki Kojima, Yukiko Shimbo, Hirofumi Suganuma, Fumiaki Maehara

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

4 Citations (Scopus)

Abstract

This paper proposes a coordinated multi-point (CoMP) transmission method based on deep learning for taxi radio systems to prevent inter-cell interference (ICI). In CoMP, it is essential to select whether to use simultaneous transmission or time division multiplexing (TDM) considering the effect of the ICI. The feature of the proposed method is to determine such a transmission mode by using vehicle position information as taxi radio systems have such position information. Moreover, the proposed method makes it possible to avoid the online system capacity calculation also required for CoMP thanks to the use of deep learning. The effectiveness of the proposed method is demonstrated in comparison with a traditional online calculation method under the practical scenario based on the taxi radio system in Japan.

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	253-256
Number of pages	4
ISBN (Electronic)	9781728149851
DOIs	https://doi.org/10.1109/ICAIIC48513.2020.9065193
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

Keywords

Taxi radio systems
coordinated multi-point (CoMP)
deep learning
multi-cell
vehicle position information

ASJC Scopus subject areas

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

Access to Document

10.1109/ICAIIC48513.2020.9065193

Cite this

Kojima, K., Shimbo, Y., Suganuma, H., & Maehara, F. (2020). Deep Learning Based CoMP Transmission Method Using Vehicle Position Information for Taxi Radio Systems. In 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020 (pp. 253-256). Article 9065193 (2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICAIIC48513.2020.9065193

Deep Learning Based CoMP Transmission Method Using Vehicle Position Information for Taxi Radio Systems. / Kojima, Kazuki; Shimbo, Yukiko; Suganuma, Hirofumi et al.
2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020. Institute of Electrical and Electronics Engineers Inc., 2020. p. 253-256 9065193 (2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020).

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

Kojima, K, Shimbo, Y, Suganuma, H & Maehara, F 2020, Deep Learning Based CoMP Transmission Method Using Vehicle Position Information for Taxi Radio Systems. in 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020., 9065193, 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Institute of Electrical and Electronics Engineers Inc., pp. 253-256, 2nd International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, 20/2/19. https://doi.org/10.1109/ICAIIC48513.2020.9065193

Kojima K, Shimbo Y, Suganuma H, Maehara F. Deep Learning Based CoMP Transmission Method Using Vehicle Position Information for Taxi Radio Systems. In 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020. Institute of Electrical and Electronics Engineers Inc. 2020. p. 253-256. 9065193. (2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020). doi: 10.1109/ICAIIC48513.2020.9065193

Kojima, Kazuki ; Shimbo, Yukiko ; Suganuma, Hirofumi et al. / Deep Learning Based CoMP Transmission Method Using Vehicle Position Information for Taxi Radio Systems. 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020. Institute of Electrical and Electronics Engineers Inc., 2020. pp. 253-256 (2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020).

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title = "Deep Learning Based CoMP Transmission Method Using Vehicle Position Information for Taxi Radio Systems",

abstract = "This paper proposes a coordinated multi-point (CoMP) transmission method based on deep learning for taxi radio systems to prevent inter-cell interference (ICI). In CoMP, it is essential to select whether to use simultaneous transmission or time division multiplexing (TDM) considering the effect of the ICI. The feature of the proposed method is to determine such a transmission mode by using vehicle position information as taxi radio systems have such position information. Moreover, the proposed method makes it possible to avoid the online system capacity calculation also required for CoMP thanks to the use of deep learning. The effectiveness of the proposed method is demonstrated in comparison with a traditional online calculation method under the practical scenario based on the taxi radio system in Japan.",

keywords = "Taxi radio systems, coordinated multi-point (CoMP), deep learning, multi-cell, vehicle position information",

author = "Kazuki Kojima and Yukiko Shimbo and Hirofumi Suganuma and Fumiaki Maehara",

note = "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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AU - Suganuma, Hirofumi

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N2 - This paper proposes a coordinated multi-point (CoMP) transmission method based on deep learning for taxi radio systems to prevent inter-cell interference (ICI). In CoMP, it is essential to select whether to use simultaneous transmission or time division multiplexing (TDM) considering the effect of the ICI. The feature of the proposed method is to determine such a transmission mode by using vehicle position information as taxi radio systems have such position information. Moreover, the proposed method makes it possible to avoid the online system capacity calculation also required for CoMP thanks to the use of deep learning. The effectiveness of the proposed method is demonstrated in comparison with a traditional online calculation method under the practical scenario based on the taxi radio system in Japan.

AB - This paper proposes a coordinated multi-point (CoMP) transmission method based on deep learning for taxi radio systems to prevent inter-cell interference (ICI). In CoMP, it is essential to select whether to use simultaneous transmission or time division multiplexing (TDM) considering the effect of the ICI. The feature of the proposed method is to determine such a transmission mode by using vehicle position information as taxi radio systems have such position information. Moreover, the proposed method makes it possible to avoid the online system capacity calculation also required for CoMP thanks to the use of deep learning. The effectiveness of the proposed method is demonstrated in comparison with a traditional online calculation method under the practical scenario based on the taxi radio system in Japan.

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Deep Learning Based CoMP Transmission Method Using Vehicle Position Information for Taxi Radio Systems

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