  In this paper, we study channel tracking for the wireless energy transfer
(WET) system, which is practically a very important, but challenging problem.
Regarding the time-varying channels as a sequence to be predicted, we exploit
the recurrent neural network (RNN) technique for channel tracking.
Particularly, combining the deep long short-term memory (LSTM) RNN with the
deep feedforward neural network, we develop a novel channel tracking scheme for
the WET system, which estimates the channel state information (CSI) at the
energy transmitter based on the previous CSI estimates, and the current and
previous harvested energy feedback information from the energy receiver.
Numerical results demonstrate the superior performance and effectiveness of the
proposed scheme.
