ChanEstNet: A Deep Learning Based Channel Estimation for High-Speed Scenarios

Liao, Yong, Hua, Yuanxiao, Dai, Xuewu, Yao, Haimei and Yang, Xinyi (2019) ChanEstNet: A Deep Learning Based Channel Estimation for High-Speed Scenarios. In: ICC 2019 - 2019 IEEE International Conference on Communications (ICC): Shanghai, China, 20-24 May 2019. IEEE, Piscataway, NJ, pp. 1272-1277. ISBN 9781538680896, 9781538680889

Liao2019_ICC2019_ChanEstNet_AcceptVersion.pdf - Accepted Version

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Aiming at the problem that the downlink channel estimation performance is limited due to the fast time-varying and non-stationary characteristics in the high-speed mobile scenarios, we propose a channel estimation network based on deep learning, called ChanEstNet. ChanEstNet uses the convolutional neural network (CNN) to extract channel response feature vectors and recurrent neural network (RNN) for channel estimation. We use a large amount of high-speed channel data to conduct offline training for the learning network, fully exploit the channel information in the training sample, make it learn the characteristics of fast time-varying and non-stationary channels, and better track the features of channels changing in high-speed environments. The simulation results show that in the high-speed mobile scenarios, compared with the traditional methods, the proposed channel estimation method has low computational complexity and significant performance improvement.

Item Type: Book Section
Additional Information: Funding information: This work was supported by the National Natural Science Foundation of China (No. 61501066), the Chongqing Frontier and Applied Basic Research Project (No. cstc2015jcyjA40003), the graduate research and innovation foundation of Chongqing, China (No. CYS18061), and the Fundamental Research Funds for the Central Universities (No. 106112017CDJXY500001).
Uncontrolled Keywords: OFDM, channel estimation, high-speed channel, deep learning, fast time-varying channel, non-stationary channel
Subjects: G500 Information Systems
G600 Software Engineering
Department: Faculties > Engineering and Environment > Mathematics, Physics and Electrical Engineering
Depositing User: Elena Carlaw
Date Deposited: 28 Jun 2021 11:22
Last Modified: 31 Jul 2021 10:35

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