Reference : Deep Learning-Aided 5G Channel Estimation
Scientific congresses, symposiums and conference proceedings : Paper published in a book
Engineering, computing & technology : Electrical & electronics engineering
Security, Reliability and Trust
http://hdl.handle.net/10993/46585
Deep Learning-Aided 5G Channel Estimation
English
[en] Deep Learning-Aided 5G Channel Estimation
Le, Ha An mailto [School of Electronics and Telecommunications, Hanoi University of Science and Technology, Hanoi, Vietnam and Department of Electrical and Computer Engineering, Seoul National University, Seoul, Korea]
Trinh, van Chien mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SigCom >]
Nguyen, Tien Hoa mailto [School of Electronics and Telecommunications, Hanoi University of Science and Technology, Hanoi, Vietnam]
Wan, Choi mailto [Department of Electrical and Computer Engineering, Seoul National University, Seoul, Korea]
Nguyen, Van Duc mailto [School of Electronics and Telecommunications, Hanoi University of Science and Technology, Hanoi, Vietnam]
6-Jan-2021
Deep Learning-Aided 5G Channel Estimation
Yes
International
The 15th International Conference on Ubiquitous Information Management and Communication
From 04-01-2021 to 06-01-2021
IEEE
Seoul
South Korea
[en] Deep Neural Networks ; Channel Estimation ; Multiple-Input Multiple-Output ; Frequency Selective Channel
[en] Deep learning has demonstrated the important roles in improving the system performance and reducing computational complexity for $5$G-and-beyond networks. In this paper, we propose a new channel estimation method with the assistance of deep learning in order to support the least-squares estimation, which is a low-cost method but having relatively high channel estimation errors. This goal is achieved by utilizing a MIMO (multiple-input multiple-output) system with a multi-path channel profile used for simulations in the 5G networks under the severity of Doppler effects. Numerical results demonstrate the superiority of the proposed deep learning-assisted channel estimation method over the other channel estimation methods in previous works in terms of mean square errors.
http://hdl.handle.net/10993/46585

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