  Nonlinear energy harvesters (EH) behave differently depending on the range of
their input power. In the literature, different models have been proposed
mainly for relatively small and large input power regimes of an EH. Due to the
complexity of the proposed nonlinear models, obtaining analytical optimal or
well performing signal designs have been extremely challenging. Relying on the
proposed models in the literature, the learning problem of modulation design
for simultaneous wireless information-power transfer (SWIPT) over a
point-to-point link is studied. Joint optimization of the transmitter and the
receiver is implemented using neural network (NN)-based autoencoders. The
results reveal that for relatively small channel input powers, as the power
demand increases at the receiver, one of the symbols is shot away from the
origin while the remaining symbols approach zero amplitude. In the very extreme
case of merely receiver power demand, the modulations are in the form of On-Off
keying signalling with a low probability of the On signal. On the other side,
for relatively large channel input powers, it is observed that as the receiver
power demand increases, a number of symbols approach zero amplitude, whereas
the others (more than one symbol) get equally high amplitudes but with
different phases. In the extreme scenario of merely receiver power demand, the
modulation resembles multiple On-Off keying signalling with different phases.
