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arXiv · 2101.06666

Deep Learning-Aided 5G Channel Estimation

An Le Ha·Trinh Van Chien·Tien Hoa Nguyen·Wan Choi·Van Duc Nguyen
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Abstract

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.

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An Le Ha, Trinh Van Chien, Tien Hoa Nguyen, Wan Choi, Van Duc Nguyen. 2021-01-17. Deep Learning-Aided 5G Channel Estimation. https://arxiv.org/abs/2101.06666

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