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The end-to-end learning of Simultaneous Wireless Information and PowerTransfer (SWIPT) over a noisy channel is studied. Adopting a nonlinear modelfor the energy harvester (EH) at the receiver, a joint optimization of thetransmitter 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. Thenumerically optimized signal constellations are inline with the previoustheoretical results. In particular, it is observed that as the receiver energydemand increases, all but one of the modulation symbols are concentrated aroundthe origin and the other symbol is shot away from the origin along either thereal or imaginary subchannel.

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