Performance evaluations of channel estimation using deep-learning based super-resolution

Daiki Maruyama, Kenji Kanai, Jiro Katto

研究成果: Conference contribution

3 被引用数 (Scopus)

抄録

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.

本文言語English
ホスト出版物のタイトル2021 IEEE 18th Annual Consumer Communications and Networking Conference, CCNC 2021
出版社Institute of Electrical and Electronics Engineers Inc.
ISBN(電子版)9781728197944
DOI
出版ステータスPublished - 2021 1月 9
イベント18th IEEE Annual Consumer Communications and Networking Conference, CCNC 2021 - Virtual, Las Vegas, United States
継続期間: 2021 1月 92021 1月 13

出版物シリーズ

名前2021 IEEE 18th Annual Consumer Communications and Networking Conference, CCNC 2021

Conference

Conference18th IEEE Annual Consumer Communications and Networking Conference, CCNC 2021
国/地域United States
CityVirtual, Las Vegas
Period21/1/921/1/13

ASJC Scopus subject areas

  • 人工知能
  • コンピュータ ネットワークおよび通信
  • コンピュータ ビジョンおよびパターン認識

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