1908.11013.pdf (2.56 MB)
Deep Learning based Channel Estimation Algorithm over Time Selective Fading Channels
journal contribution
posted on 2019-09-27, 09:42 authored by Qinbo Bai, Jintao Wang, Yue Zhang, Jian SongThe 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, 2019Author affiliation
/Organisation/COLLEGE OF SCIENCE AND ENGINEERING/Department of EngineeringVersion
- AM (Accepted Manuscript)