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Deep-Learning Based Channel Estimation for OFDM Wireless Communications

Tian, Guoda LU ; Cai, Xuesong LU ; Zhou, Tian ; Wang, Weinan and Tufvesson, Fredrik LU orcid (2022)
Abstract
Multi-carrier technique is a backbone for modern commercial networks. However, the performances of multi-carrier systems in general depend greatly on the qualities of acquired channel state information (CSI). In this paper, we propose a novel deep-learning based processing pipeline to estimate CSI for payload time-frequency resource elements. The proposed pipeline contains two cascaded subblocks, namely, an initial denoise network (IDN), and a resolution enhancement network (REN). In brief, IDN applies a novel two-steps denoising structure while REN consists of pure fully-connected layers. Compared to existing works, our proposed processing architecture is more robust under lower signal-to-noise scenarios and delivers generally a... (More)
Multi-carrier technique is a backbone for modern commercial networks. However, the performances of multi-carrier systems in general depend greatly on the qualities of acquired channel state information (CSI). In this paper, we propose a novel deep-learning based processing pipeline to estimate CSI for payload time-frequency resource elements. The proposed pipeline contains two cascaded subblocks, namely, an initial denoise network (IDN), and a resolution enhancement network (REN). In brief, IDN applies a novel two-steps denoising structure while REN consists of pure fully-connected layers. Compared to existing works, our proposed processing architecture is more robust under lower signal-to-noise scenarios and delivers generally a significant gain. (Less)
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author
; ; ; and
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
in press
subject
host publication
2022 IEEE 23rd International Workshop on Signal Processing Advances in Wireless Communication (SPAWC)
external identifiers
  • scopus:85136071003
DOI
10.1109/SPAWC51304.2022.9834008
language
English
LU publication?
yes
id
d00f91b3-346d-4367-a2a8-2c61d33da3c9
date added to LUP
2022-05-13 11:23:11
date last changed
2022-09-23 13:39:39
@inproceedings{d00f91b3-346d-4367-a2a8-2c61d33da3c9,
  abstract     = {{Multi-carrier technique is a backbone for modern commercial networks. However, the performances of multi-carrier systems in general depend greatly on the qualities of acquired channel state information (CSI). In this paper, we propose a novel deep-learning based processing pipeline to estimate CSI for payload time-frequency resource elements. The proposed pipeline contains two cascaded subblocks, namely, an initial denoise network (IDN), and a resolution enhancement network (REN). In brief, IDN applies a novel two-steps denoising structure while REN consists of pure fully-connected layers. Compared to existing works, our proposed processing architecture is more robust under lower signal-to-noise scenarios and delivers generally a significant gain.}},
  author       = {{Tian, Guoda and Cai, Xuesong and Zhou, Tian and Wang, Weinan and Tufvesson, Fredrik}},
  booktitle    = {{2022 IEEE 23rd International Workshop on Signal Processing Advances in Wireless Communication (SPAWC)}},
  language     = {{eng}},
  title        = {{Deep-Learning Based Channel Estimation for OFDM Wireless Communications}},
  url          = {{http://dx.doi.org/10.1109/SPAWC51304.2022.9834008}},
  doi          = {{10.1109/SPAWC51304.2022.9834008}},
  year         = {{2022}},
}