A Learning Approach To Wireless Information and Power Transfer Signal and System Design

Abstract

The end-to-end learning of Simultaneous Wireless Information and Power Transfer (SWIPT) over a noisy channel is studied. Adopting a nonlinear model for the energy harvester (EH) at the receiver, a joint optimization of the transmitter and the receiver is implemented using Deep Neural Network (DNN)-based autoencoders. Modulation constellations for different levels of "power" and "information rate" demand at the receiver are obtained. The numerically optimized signal constellations are inline with the previous theoretical results. In particular, it is observed that as the receiver energy demand increases, all but one of the modulation symbols are concentrated around the origin and the other symbol is shot away from the origin along either the real or imaginary subchannel.