Name
Title | ||
|---|---|---|
Performance Evaluations of Channel Estimation Using Deep-learning Based Super-resolution |
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/CCNC49032.2021.9369521 | 2021 IEEE 18th Annual Consumer Communications & Networking Conference (CCNC) |
Keywords | DocType | ISSN |
channel estimation,5g system,super-resolution,deep learning,physical layer | Conference | 2331-9852 |
ISBN | Citations | PageRank |
978-1-7281-9795-1 | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Daiki Maruyama | 1 | 0 | 0.34 |
| Kenji Kanai | 2 | 24 | 18.26 |
| Jiro Katto | 3 | 262 | 66.14 |