Waveform optimization for radio-frequency wireless power transfer : (Invited paper)

Moghadam, Mohammad R. Vedady; Zeng, Yong; Zhang, Rui

doi:10.1109/spawc.2017.8227719

Cited by 27 publications

(38 citation statements)

References 18 publications

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“…Let x 1 satisfies |x 1 | < σ x < A and I 0 √ 2Bh E x 1 > E req and let F 1 be the unit-step function at x 1 , then g 1 (F 1 ) = x 2 1 − σ 2 x < 0 and g 2 (F 1 ) = −I 0 √ 2Bh E x 1 + E req < 0, hence Slater's condition holds. From the Lagrangian theorem, we conclude that strong duality holds and there exist λ 1 ≥ 0 and λ 2 ≥ 0 such that the expression for the capacity in (8) holds and is achieved also by F 0 . Moreover, the complementary slackness conditions λ 1 g 1 (F 0 ) = 0 and λ 2 g 2 (F 0 ) = 0 must hold.…”

Section: Appendix a − Proof Of Theoremmentioning

confidence: 82%

“…We consider a setup with f c = 2.45 GHz, α = 2.5, d I = 500 m, and d E = 70 m. At the ID receiver, we assume a noise power of σ 2 n = −80 dBm. At the EH receiver, we assume the following circuit parameters R ant = 50 Ω, i s = 100 µA, η = 1.5, V T = 25.85 mV, and R L = 10 kΩ [4], [8].…”

Section: Numerical Resultsmentioning

confidence: 99%

“…Hence, the received signal is y E (t) = x(t)h E in the baseband and y RF E (t) = √ 2 {y E (t)e j2πfct } in the RF domain, where j is the imaginary unit, f c is the carrier frequency, and {·} denotes the real part of a complex number. Next, we focus on the EH receiver and adopt the nonlinear rectenna model from [8]. In particular, we obtain an expression for the harvested energy at the EH receiver in terms of the input distribution for the considered scalar single-carrier AWGN channel.…”

Section: A System Modelmentioning

confidence: 99%

“…Next, the proof that the capacity C = sup F ∈Ω I(F ) is equivalently given by (8) follows from the Lagrangian theorem for constrained optimization. In particular, this equivalence (strong duality) holds for the convex problem in (7) if C is finite and Slater's condition holds, i.e., there exists an interior point F ∈ F A such that all constraints hold with strict inequality, i.e., g i (F ) < 0, i = 1, 2.…”

Section: Appendix a − Proof Of Theoremmentioning

confidence: 99%

“…In particular, we consider a three-node SWIPT system, in which one receiver harvests energy and another separate receiver decodes information from a signal broadcasted by a common transmitter over an additive white Gaussian noise (AWGN) channel. For the EH receiver, we adopt the nonlinear rectenna circuit model from [4], [8]. Our objective is to find the optimal input distribution of the transmit signal that maximizes the mutual information between the input and the output of the information channel under a minimum harvested power constraint at the EH receiver.…”

Section: Introductionmentioning

confidence: 99%

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On the Capacity of SWIPT Systems with a Nonlinear Energy Harvesting Circuit

Morsi

Jamali

et al. 2018

2018 IEEE International Conference on Communications (ICC)

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In this paper, we study information-theoretic limits for simultaneous wireless information and power transfer (SWIPT) systems employing a practical nonlinear radio frequency (RF) energy harvesting (EH) receiver. In particular, we consider a three-node system with one transmitter that broadcasts a common signal to separated information decoding (ID) and EH receivers. Owing to the nonlinearity of the EH receiver circuit, the efficiency of wireless power transfer depends significantly on the waveform of the transmitted signal. In this paper, we aim to answer the following fundamental question: What is the optimal input distribution of the transmit waveform that maximizes the rate of the ID receiver for a given required harvested power at the EH receiver? In particular, we study the capacity of a SWIPT system impaired by additive white Gaussian noise (AWGN) under average-power (AP) and peak-power (PP) constraints at the transmitter and an EH constraint at the EH receiver. Using Hermite polynomial bases, we prove that the optimal capacityachieving input distribution that maximizes the rate-energy region is unique and discrete with a finite number of mass points. Furthermore, we show that the optimal input distribution for the same problem without PP constraint is discrete whenever the EH constraint is active and continuous zero-mean Gaussian, otherwise. Our numerical results show that the rate-energy region is enlarged for a larger PP constraint and that the rate loss of the considered SWIPT system compared to the AWGN channel without EH receiver is reduced by increasing the AP budget.

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Section: Appendix a − Proof Of Theoremmentioning

confidence: 82%

Section: Numerical Resultsmentioning

confidence: 99%

Section: A System Modelmentioning

confidence: 99%

Section: Appendix a − Proof Of Theoremmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

On the Capacity of SWIPT Systems with a Nonlinear Energy Harvesting Circuit

Morsi

Jamali

et al. 2018

2018 IEEE International Conference on Communications (ICC)

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Resource Allocation for Secure UAV-Assisted SWIPT Systems

et al. 2019

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The application of unmanned aerial vehicles (UAVs) is promising to improve the energy harvesting (EH) efficiency of simultaneous wireless and information and power transfer (SWIPT) system. However, the secure communications are challenging in UAV-assisted SWIPT systems due to the high probability of the existence of line-of-sight links between the UAV and eavesdroppers and the broadcast nature of SWIPT. In order to overcome it, a resource allocation problem is studied in an UAV-assisted SWIPT system, where the multiple eavesdroppers exist. The secrecy rate is maximized by jointly optimizing the trajectory and transmit power of the UAV. An alternative optimization algorithm is proposed to tackle the challenging non-convex problem. The simulation results demonstrate that our proposed resource allocation scheme outperforms other benchmark schemes in terms of the average secrecy rate. It is shown that our proposed algorithm is efficient to converge. INDEX TERMS Unmanned aerial vehicles, physical layer security, SWIPT, resource allocation, energy harvesting. FUHUI ZHOU received the Ph.D. degree from Xidian University, Xi'an, China, in 2016. He is currently a Senior Research Fellow with Utah State University. He is also an Associate Professor with the School of Information Engineering, Nanchang University. He has worked as an international visiting Ph.D. student of the University of British Columbia, from 2015 to 2016.

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Phase Aligned Time-Reversal for Multi-User Wireless Power Transfer Systems With Non-Linear Energy Harvesting

Hwang

Park

2021

IEEE Access

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In this paper, we study a waveform design based on time-reversal (TR) for multi-user wireless power transfer (WPT) systems in multipath channels. The existing waveforms for WPT using the nonlinear energy harvesting (EH) model have been designed in the frequency domain, whereas we design the waveform directly in the time domain by modifying TR to be suitable for WPT. In the non-linear EH model, the peak signal with the largest magnitude among the received signals is responsible for most of the harvested energy at the energy receiver (ER). Since the peak signal of TR for multi-user consists of the sum of the inter-user interference (IUI) signals and the desired signal, the phase difference between these signals can reduce the magnitude of the peak signal. Thus, we propose a novel waveform based on TR, called a phase aligned TR (PATR). The objective of the proposed PATR is to increase the harvested energy by aligning the phase between the IUI signal and desired signal as in-phase. We derive the optimal phase set to be pre-rotated for maximizing the peak signal, and design the PATR waveform by combining the modified TR and the derived optimal phase set. We prove the superior performance of the proposed PATR compared to the existing schemes in the multi-user WPT systems with the non-linear EH model by simulation. Moreover, we show that the gain for the harvested energy of the proposed PATR increases as the number of transmit antennas and the number of ERs increase.INDEX TERMS Wireless power transfer, energy harvesting, time-reversal, non-linear energy harvesting circuit, interference alignment.

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Waveform optimization for radio-frequency wireless power transfer : (Invited paper)

Cited by 27 publications

References 18 publications

On the Capacity of SWIPT Systems with a Nonlinear Energy Harvesting Circuit

On the Capacity of SWIPT Systems with a Nonlinear Energy Harvesting Circuit

Resource Allocation for Secure UAV-Assisted SWIPT Systems

Phase Aligned Time-Reversal for Multi-User Wireless Power Transfer Systems With Non-Linear Energy Harvesting

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