Deep Neural Networks; Channel Estimation; Multiple-Input Multiple-Output; Frequency Selective Channel
Résumé :
[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.
Disciplines :
Ingénierie électrique & électronique
Auteur, co-auteur :
Le, Ha An; 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 ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SigCom
Nguyen, Tien Hoa; School of Electronics and Telecommunications, Hanoi University of Science and Technology, Hanoi, Vietnam
Wan, Choi; Department of Electrical and Computer Engineering, Seoul National University, Seoul, Korea
Nguyen, Van Duc; School of Electronics and Telecommunications, Hanoi University of Science and Technology, Hanoi, Vietnam
Co-auteurs externes :
yes
Langue du document :
Anglais
Titre :
Deep Learning-Aided 5G Channel Estimation
Titre traduit :
[en] Deep Learning-Aided 5G Channel Estimation
Date de publication/diffusion :
06 janvier 2021
Nom de la manifestation :
The 15th International Conference on Ubiquitous Information Management and Communication
