Opportunistic Waveform Scheduling for Wireless Power Transfer With Multiple Devices

Kim, Kyeong‐Won; Lee, Hyun-Suk; Lee, Jang-Won

doi:10.1109/twc.2020.2994161

Cited by 15 publications

(13 citation statements)

References 32 publications

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“…The key takeaway here is that, by optimizing the waveform to jointly transfer power to multiple users simultaneously, we obtain an energy region ("weighted sum") that is larger than that achieved by a time-sharing approach, such as time-division multiple access (TDMA), where the transmit waveform and beamforming is optimized for a single user at a time and each user is scheduled to receive energy during a fraction of the time. Other multi-user waveform designs have appeared in [107], [108].…”

Section: F Signal Processing Techniques For Multi-user Wptmentioning

confidence: 99%

Wireless Power Transfer for Future Networks: Signal Processing, Machine Learning, Computing, and Sensing

Clerckx

Huang

Varshney

et al. 2021

IEEE J. Sel. Top. Signal Process.

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Wireless power transfer (WPT) is an emerging paradigm that will enable using wireless to its full potential in future networks, not only to convey information but also to deliver energy. Such networks will enable trillions of future low-power devices to sense, compute, connect, and energize anywhere, anytime, and on the move. The design of such future networks brings new challenges and opportunities for signal processing, machine learning, sensing, and computing so as to make the best use of the RF radiations, spectrum, and network infrastructure in providing cost-effective and realtime power supplies to wireless devices and enable wirelesspowered applications. In this paper, we first review recent signal processing techniques to make WPT and wireless information and power transfer (WIPT) as efficient as possible. Topics include high-power amplifier and energy harvester nonlinearities, active and passive beamforming, intelligent reflecting surfaces, receive combining with multi-antenna harvester, modulation, coding, waveform, large-scale (massive) multiple-input multipleoutput (MIMO), channel acquisition, transmit diversity, multiuser power region characterization, coordinated multipoint, and distributed antenna systems. Then, we overview two different design methodologies: the model and optimize approach relying on analytical system models, modern convex optimization, and communication/information theory, and the learning approach based on data-driven end-to-end learning and physics-based learning. We discuss the pros and cons of each approach, especially when accounting for various nonlinearities in wireless-powered networks, and identify interesting emerging opportunities for the approaches to complement each other. Finally, we identify new emerging wireless technologies where WPT may play a key role-wireless-powered mobile edge computing and wirelesspowered sensing-arguing WPT, communication, computation, and sensing must be jointly designed.

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Section: F Signal Processing Techniques For Multi-user Wptmentioning

confidence: 99%

Wireless Power Transfer for Future Networks: Signal Processing, Machine Learning, Computing, and Sensing

Clerckx

Huang

Varshney

et al. 2021

IEEE J. Sel. Top. Signal Process.

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“…We denote the beamforming vector of single-lobe beam pattern l by a l , which represents the magnitudes and phases of the antenna elements of the PB for single-lobe beam pattern l. Note that it satisfies ||a l || 2 = 1. Then, multi-lobe beam patterns can be generated by allocating the weights (transmit power) 3 to the K beams. We provide the examples of beam patterns with the 8element antenna array in Fig.…”

Section: System Model and Problem Formulation A System Modelmentioning

confidence: 99%

“…We now formulate a beam scheduling problem for WPT to maximize the expected number of the power requesting devices whose thresholds are satisfied, which can be simply denoted by the sum of the PTIs of all devices in (3). At the beginning of time-slot t, the PB observes a context x t {x t n } n∈[N ] (i.e., the power requests of the devices).…”

Section: B Beam Scheduling Problem For Wptmentioning

confidence: 99%

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Adaptive Wireless Power Transfer Beam Scheduling for Non-Static IoT Devices Using Deep Reinforcement Learning

Lee

2020

IEEE Access

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“…By taking the rectenna nonlinearity into account, [7]- [14] have studied the power waveform design problem for farfield WPT by adopting the multisine waveform structure for the transmit signal. In [7]- [11], the rectenna nonlinearity is approximately characterized via the second and higher order terms in the truncated Taylor expansion of the diode output current in terms of the input voltage.…”

Section: Introductionmentioning

confidence: 99%

Refined Nonlinear Rectenna Modeling and Optimal Waveform Design for Multi-User Multi-Antenna Wireless Power Transfer

Abeywickrama¹,

Zhang²,

Yuen³

2020

Preprint

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In this paper, we study the optimal waveform design for wireless power transfer (WPT) from a multi-antenna energy transmitter (ET) to multiple single-antenna energy receivers (ERs) simultaneously in multi-path frequency-selective channels. First, we propose a refined nonlinear current-voltage model of the diode in the ER rectifier, and accordingly derive new expressions for the output direct current (DC) voltage and corresponding harvested power at the ER. Leveraging this new rectenna model, we first consider the single-ER case and study the multisinebased power waveform design based on the wireless channel to maximize the harvested power at the ER. We propose two efficient algorithms for finding high-quality suboptimal solutions to this non-convex optimization problem. Next, we extend our formulated waveform design problem to the general multi-ER case for maximizing the weighted sum of the harvested powers by all ERs, and propose an efficient difference-of-convexfunctions programming (DCP)-based algorithm for solving this problem. Finally, we demonstrate the superior performance of our proposed waveform designs based on the new rectenna model over existing schemes/models via simulations.

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Opportunistic Waveform Scheduling for Wireless Power Transfer With Multiple Devices

Cited by 15 publications

References 32 publications

Wireless Power Transfer for Future Networks: Signal Processing, Machine Learning, Computing, and Sensing

Wireless Power Transfer for Future Networks: Signal Processing, Machine Learning, Computing, and Sensing

Adaptive Wireless Power Transfer Beam Scheduling for Non-Static IoT Devices Using Deep Reinforcement Learning

Refined Nonlinear Rectenna Modeling and Optimal Waveform Design for Multi-User Multi-Antenna Wireless Power Transfer

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