A channel estimation method using denoising autoencoder for large-scale asymmetric backscatter systemsopen access
- Authors
- Jung, Chae Yoon; Kang, Jae-Mo; Kim, Dong In
- Issue Date
- Apr-2024
- Publisher
- Korean Institute of Communications and Information Sciences
- Keywords
- Backscatter communication; Beamforming; Channel estimation; Deep learning; Denoising autoencoder
- Citation
- ICT Express, v.10, no.2, pp 400 - 405
- Pages
- 6
- Indexed
- SCIE
SCOPUS
KCI
- Journal Title
- ICT Express
- Volume
- 10
- Number
- 2
- Start Page
- 400
- End Page
- 405
- URI
- https://scholarworks.bwise.kr/skku/handle/2021.sw.skku/109036
- DOI
- 10.1016/j.icte.2023.09.002
- ISSN
- 2405-9595
2405-9595
- Abstract
- A novel channel estimation method based on deep learning algorithm is proposed for large-scale IoT networks. We consider asymmetric backscatter communication system to maintain low-power at sensor nodes. In order to obtain channel data, we design denoising autoencoder which consists of encoder with Feedforward Neural Network (FNN) and decoder with Convolutional Neural Network (CNN). Finally, the channel estimation error is minimized, while the pilots are optimized. Especially, we adopt beamforming technique that relies only on cascaded channel data to reduce complexity in multi-sensor system. It is shown that the accuracy is slightly degraded while the complexity is greatly reduced. © 2023 The Author(s)
- Files in This Item
- There are no files associated with this item.
- Appears in
Collections - Information and Communication Engineering > School of Electronic and Electrical Engineering > 1. Journal Articles
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.