Deep-Learning Based Channel Estimation for OFDM Wireless Communications
(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)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/d00f91b3-346d-4367-a2a8-2c61d33da3c9
- author
- Tian, Guoda LU ; Cai, Xuesong LU ; Zhou, Tian ; Wang, Weinan and Tufvesson, Fredrik LU
- organization
- publishing date
- 2022
- 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}}, }