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

Abstract

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.