TY - GEN
T1 - Performance evaluations of channel estimation using deep-learning based super-resolution
AU - Maruyama, Daiki
AU - Kanai, Kenji
AU - Katto, Jiro
N1 - Funding Information:
ACKNOWLEDGMENT This paper is support by the verification-style research & development program for solving reginal challenges using data cooperation and utilization by NICT, Japan and partially supported by the R&D contract “Wired-and-Wireless Converged Radio Access Network for Massive IoT Traffic” with the Ministry of Internal Affairs and Communications, Japan, for radio resource enhancement.
Publisher Copyright:
© 2021 IEEE.
PY - 2021/1/9
Y1 - 2021/1/9
N2 - Thanks to breakthrough and evolution of deep learning in computer vision areas, adaptation of deep learning into communication systems are getting lots of attention to researchers. Recently, a channel estimation method by using a deep learning-based image super-resolution (SR) technique, namely ChannelNet, has been proposed. Inspired by this research, in this paper, we propose a deep SR based channel estimation method by applying more accurate deep learning-based SR network architecture, EDSR. In order to enhance intelligibility and reliability of deep SR based channel estimation methods, we evaluate the performance of several deep SR based channel estimation methods (SRCNN, ChannelNet and EDSR) by carrying out practical 5G simulations. From the evaluations, the results conclude that the deep SR based channel estimation methods can potentially improve accuracy of channel estimation and reduce BER characteristics.
AB - Thanks to breakthrough and evolution of deep learning in computer vision areas, adaptation of deep learning into communication systems are getting lots of attention to researchers. Recently, a channel estimation method by using a deep learning-based image super-resolution (SR) technique, namely ChannelNet, has been proposed. Inspired by this research, in this paper, we propose a deep SR based channel estimation method by applying more accurate deep learning-based SR network architecture, EDSR. In order to enhance intelligibility and reliability of deep SR based channel estimation methods, we evaluate the performance of several deep SR based channel estimation methods (SRCNN, ChannelNet and EDSR) by carrying out practical 5G simulations. From the evaluations, the results conclude that the deep SR based channel estimation methods can potentially improve accuracy of channel estimation and reduce BER characteristics.
KW - 5g system
KW - Channel estimation
KW - Deep learning
KW - Physical layer
KW - Super-resolution
UR - http://www.scopus.com/inward/record.url?scp=85102982147&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85102982147&partnerID=8YFLogxK
U2 - 10.1109/CCNC49032.2021.9369521
DO - 10.1109/CCNC49032.2021.9369521
M3 - Conference contribution
AN - SCOPUS:85102982147
T3 - 2021 IEEE 18th Annual Consumer Communications and Networking Conference, CCNC 2021
BT - 2021 IEEE 18th Annual Consumer Communications and Networking Conference, CCNC 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 18th IEEE Annual Consumer Communications and Networking Conference, CCNC 2021
Y2 - 9 January 2021 through 13 January 2021
ER -