TY - GEN
T1 - Deep Learning Based CoMP Transmission Method Using Vehicle Position Information for Taxi Radio Systems
AU - Kojima, Kazuki
AU - Shimbo, Yukiko
AU - Suganuma, Hirofumi
AU - Maehara, Fumiaki
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/2
Y1 - 2020/2
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.
KW - Taxi radio systems
KW - coordinated multi-point (CoMP)
KW - deep learning
KW - multi-cell
KW - vehicle position information
UR - http://www.scopus.com/inward/record.url?scp=85084074978&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85084074978&partnerID=8YFLogxK
U2 - 10.1109/ICAIIC48513.2020.9065193
DO - 10.1109/ICAIIC48513.2020.9065193
M3 - Conference contribution
AN - SCOPUS:85084074978
T3 - 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020
SP - 253
EP - 256
BT - 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2nd International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020
Y2 - 19 February 2020 through 21 February 2020
ER -