Learning Modulation Design for SWIPT with Nonlinear Energy Harvester: Large and Small Signal Power Regimes
Morteza Varasteh, Jakob Hoydis, Bruno Clerckx

TL;DR
This paper investigates modulation design for SWIPT systems with nonlinear energy harvesters across different power regimes, using neural networks to optimize transmitter and receiver configurations, revealing distinct modulation strategies for small and large input powers.
Contribution
It introduces a neural network-based autoencoder approach to optimize modulation schemes for nonlinear energy harvesters in SWIPT, considering both small and large power regimes, which is a novel application.
Findings
Small power regime: symbols are pushed away from the origin as power demand increases.
Large power regime: multiple symbols approach high amplitude with different phases as power demand increases.
Extreme case: modulation resembles On-Off keying signaling with low On probability.
Abstract
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…
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Taxonomy
TopicsEnergy Harvesting in Wireless Networks · Wireless Power Transfer Systems · Full-Duplex Wireless Communications
