Practical Non-Linear Energy Harvesting Model and Resource Allocation for SWIPT Systems

By Elena Boshkovska, Derrick Wing Kwan Ng, Nikola Zlatanov, Robert Schober. IEEE Communications Letters, 2015

996 citations recorded by the source corpus.

In this letter, we propose a practical non-linear energy harvesting model and design a resource allocation algorithm for simultaneous wireless information and power transfer (SWIPT) systems. The algorithm design is formulated as a non-convex optimization problem for the maximization of the total harvested power at energy harvesting receivers subject to minimum required signal-to-interference-plus-noise ratios (SINRs) at multiple information receivers. We transform the considered non-convex objective function from sum-of-ratios form into an equivalent objective function in subtractive form, which enables the derivation of an efficient iterative resource allocation algorithm. In each iteration, a rank-constrained semidefinite program (SDP) is solved optimally by SDP relaxation. Numerical results unveil a substantial performance gain that can be achieved if the resource allocation design is based on the proposed non-linear energy harvesting model instead of the traditional linear model.

Subfields: Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/bc319602da2b1944a30c00fb503e5d201f04adaf

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Citations in the corpus

Massive Access for 5G and Beyond (2020, background): “Recently, wireless energy transfer, namely wireless charging, has received considerable attention from both academia and industry [184]–[186].”
On the Role of Age of Information in the Internet of Things (2018, methodology): “Although the conventional linear RF energy harvesting model used in this article is highly tractable, it may not always be accurate [15].”
Communications and Signals Design for Wireless Power Transmission (2016, background): “Some results on the input power mismatch and its implication on system design can be found in [158].”
Joint offloading and computing optimization in wireless powered mobile-edge computing systems (2017, background): “veform. In the literature, there have been a handful of recent works on analytic nonlinear EH models, which characterize such nonlinear relations between the harvested DC power and the input RF power [23] or transmit waveform [24]. However, there still lacks a generic EH model that captures all prac”
Intelligent Reflecting Surface Aided MIMO Broadcasting for Simultaneous Wireless Information and Power Transfer (2019, background): “This nonlinear EH model has been characterized in [34], which is a complex function of the RF power.”
Throughput Maximization for UAV-Enabled Wireless Powered Communication Networks (2018, background): “Note that in practice, the RF-to-DC energy conversion efficiency is generally non-linear and depends on the received RF power level [36] and signal waveform [37].”
5G D2D Networks: Techniques, Challenges, and Future Prospects (2018, neutral): “in harvesting efficiency [120].”
UAV-Enabled Wireless Power Transfer: Trajectory Design and Energy Optimization (2017, background): “In practice, the RF-to-DC conversion is generally non-linear and the conversion efficiency critically depends on the received RF power and waveform at the ER (see, [4], [15], [22]).”
Fundamentals of Wireless Information and Power Transfer: From RF Energy Harvester Models to Signal and System Designs (2018, methodology): “The saturation nonlinear model is a tractable parametric model proposed in [47], and is applicable to SWIPT systems for a given pre-defined signal waveform and only based on the average received RF power P r rf .”
AoI-Minimal Trajectory Planning and Data Collection in UAV-Assisted Wireless Powered IoT Networks (2020, methodology): “It is noticed that in the simulations, the parameters associated with the nonlinear EH model are set according to [46], [47], which were obtained by measuring practical EH circuits.”