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Deep Learning based Channel Estimation Algorithm over Time Selective Fading Channels

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journal contribution
posted on 27.09.2019, 09:42 by Qinbo Bai, Jintao Wang, Yue Zhang, Jian Song
The research about deep learning application for physical layer has been received much attention in recent years. In this paper, we propose a Deep Learning (DL) based channel estimator under time varying Rayleigh fading channel. We build up, train and test the channel estimator using Neural Network (NN). The proposed DL-based estimator can dynamically track the channel status without any prior knowledge about the channel model and statistic characteristics. The simulation results show the proposed NN estimator has better Mean Square Error (MSE) performance compared with the traditional algorithms and some other DL-based architectures. Furthermore, the proposed DL-based estimator also shows its robustness with the different pilot densities.

Funding

This work was supported in part by the National Key R&D Program of China under Grant 2017YFE0112300 and Beijing National Research Center for Information Science and Technology under Grant BNR2019RC01014 and BNR2019TD01001 and EU Horizon 2020 project grant number 761992 (IoRL).(Corresponding author: Jintao Wang.)

History

Citation

IEEE Transactions on Cognitive Communications and Networking, 2019

Author affiliation

/Organisation/COLLEGE OF SCIENCE AND ENGINEERING/Department of Engineering

Version

AM (Accepted Manuscript)

Published in

IEEE Transactions on Cognitive Communications and Networking

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

eissn

2332-7731

Copyright date

2019

Available date

27/09/2019

Publisher version

https://ieeexplore.ieee.org/document/8847452

Language

en