Generalized Regression Neural Network Based Channel Identification and Compensation Using Scattered Pilot

In the high-speed mobile environment, channel state information (CSI) estimated at the beginning of the packet is quite different at the last part because the actual channel state changes with time. To overcome this problem, a neural network (NN) based channel compensation method was previously developed. Due to inaccurate channel estimation of decision feedback channel estimation (DFCE), the pilot-aided CSI of the first symbol and DFCE-aided CSIs in the intermediate data part will cause inexact channel state transition even though the application of NN. Accordingly, the channel compensation performance is still degraded, especially in the last part of the packet. This paper proposes a new version of GRNN based channel identification and compensation method by introducing scattered pilot. It can improve the tracking capability of GRNN thanks to densely arranged pilot in the time-domain while it cannot reduce the transmission efficiency. Simulation results show that the proposed method is more effective than the conventional ones in terms of RMSE and BER performance, even in the fast fading environment.

Keywords

OFDM, fast fading, channel estimation, generalized regression neural network, DFCE, scattered pilot

Citation

Radioengineering. 2021 vol. 30, č. 4, s. 695-703. ISSN 1210-2512
https://www.radioeng.cz/fulltexts/2021/21_04_0695_0703.pdf

Document type

Peer-reviewed

Document version

Published version

Language of document

en

Document licence

Creative Commons Attribution 4.0 International license
http://creativecommons.org/licenses/by/4.0/