Deep RNN-Based Channel Tracking for Wireless Energy Transfer System

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