Neural network based channel identification and compensation

This letter proposes a neural network based channel identification and compensation methods for an OFDM system. Under the fast fading environment, pilot-aided channel estimation suffers from channel state fluctuation particularly in the last part of the packet. The proposed approach can estimate the whole transition of channel states and efficiently compensate the channel variation using the generalization capability of a neural network. The computer simulation results clarify its effectiveness via improved BER performance even under the stringent Doppler shift.