Deep Learning-Aided 5G Channel Estimation

2021/01/17 by Ha, An Le, Van Chien, Trinh, Nguyen, Tien Hoa +2 · 2 citations
#FOS: Computer and information sciences #Information Theory (cs.IT)

paper · doi:10.48550/arxiv.2101.06666

Abstract

Deep learning has demonstrated the important roles in improving the system performance and reducing computational complexity for 5G-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.

Cited by

• Sparse MIMO-OFDM Channel Estimation via RKHS Regularization
• ReQuestNet: A Foundational Learning model for Channel Estimation

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