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Nonlinear energy harvesters (EH) behave differently depending on the range oftheir input power. In the literature, different models have been proposedmainly for relatively small and large input power regimes of an EH. Due to thecomplexity of the proposed nonlinear models, obtaining analytical optimal orwell performing signal designs have been extremely challenging. Relying on theproposed models in the literature, the learning problem of modulation designfor simultaneous wireless information-power transfer (SWIPT) over apoint-to-point link is studied. Joint optimization of the transmitter and thereceiver is implemented using neural network (NN)-based autoencoders. Theresults reveal that for relatively small channel input powers, as the powerdemand increases at the receiver, one of the symbols is shot away from theorigin while the remaining symbols approach zero amplitude. In the very extremecase of merely receiver power demand, the modulations are in the form of On-Offkeying 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 receiverpower demand increases, a number of symbols approach zero amplitude, whereasthe others (more than one symbol) get equally high amplitudes but withdifferent phases. In the extreme scenario of merely receiver power demand, themodulation resembles multiple On-Off keying signalling with different phases.

Learning Modulation Design for SWIPT with Nonlinear Energy Harvester: Large and Small Signal Power Regimes