  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.
