Resource Allocation for Simultaneous Wireless Information and Power Transfer Systems: A Tutorial Overview

Wei, Zhiqiang; Yu, Xianghao; Ng, Derrick Wing Kwan; Schober, Robert

doi:10.1109/jproc.2021.3120888

Cited by 48 publications

(14 citation statements)

References 120 publications

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“…1 Proof: Please refer to Appendix C. With a suitable selection of A i , e.g., A i = −I M , the minimization can be turned into a maximization. For instance, the proposed framework can capture the weighted sum-power transfer maximization in [9], [17], the wireless-information transfer efficiency maximization in [10], the intended signal power maximization in [12], etc.…”

Section: A Rank-one Optimization Frameworkmentioning

confidence: 99%

“…Thanks to its capable of providing accurate or even near optimal approximations [1], the SDR technique has been exploited for QCQPs representing beamforming problems for simultaneous wireless information and power transfer (SWIPT), e.g., [9], [10], physical-layer security for SWIPT, e.g., [11], [12], intelligent reflecting surface (IRS), e.g., [13]- [15], and IRS-Aided SWIPT, e.g., [16], [17]. With respect to SWIPT scenarios, adopting an assumption that all users' channel vectors are mutually and statically independent, reference [9] considered the maximization of a weighted sumpower transferred to all the energy receivers while reference [10] studied a wireless-information-transfer maximization with both linear and non-linear energy-harvesting models. It was shown in [9] and [10] when the considered channels are statistically independent with each other, with probability one, the beamforming matrices for the information receivers are rank one while those for the energy receivers have rank less or equal one.…”

Section: Introductionmentioning

confidence: 99%

“…With respect to SWIPT scenarios, adopting an assumption that all users' channel vectors are mutually and statically independent, reference [9] considered the maximization of a weighted sumpower transferred to all the energy receivers while reference [10] studied a wireless-information-transfer maximization with both linear and non-linear energy-harvesting models. It was shown in [9] and [10] when the considered channels are statistically independent with each other, with probability one, the beamforming matrices for the information receivers are rank one while those for the energy receivers have rank less or equal one. Considering physical-layer security for SWIPT scenarios, it was proved that the SDR counterpart of a QCQP proposed in [11], i.e., a total-transmit-power minimization problem, and the SDR counterpart of a QCQP introduced in [12], i.e., the maximization problem of the intended signal power at every information receiver and the total received power at each energy harvesting receiver, yield rank-one optimal solutions if they are feasible.…”

Section: Introductionmentioning

confidence: 99%

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A Rank-One Optimization Framework and Its Applications to Transmit Beamforming

Lê

Yang

2024

IEEE Trans. Veh. Technol.

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This paper proposes an elegant optimization framework consisting of a mix of linear-matrix-inequality and secondorder-cone constraints. The proposed framework generalizes the semidefinite relaxation (SDR) enabled solution to the typical transmit beamforming problems presented in the form of quadratically constrained quadratic programs (QCQPs) in the literature. It is proved that the optimization problems subsumed under the framework always admit a rank-one optimal solution when they are feasible and their optimal solutions are not trivial. This finding indicates that the relaxation is tight as the optimal solution of the original beamforming QCQP can be straightforwardly obtained from that of the SDR counterpart without any loss of optimality. Four representative examples of transmit beamforming, i.e., transmit beamforming with perfect channel state information (CSI), transmit beamforming with imperfect CSI, chance-constraint approach for imperfect CSI, and reconfigurable-intelligent-surface (RIS) aided beamforming, are shown to demonstrate how the proposed optimization framework can be realized in deriving the SDR counterparts for different beamforming designs.

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Section: A Rank-one Optimization Frameworkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Rank-One Optimization Framework and Its Applications to Transmit Beamforming

Lê

Yang

2024

IEEE Trans. Veh. Technol.

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“…The user devices employ the harvested power for sensing, signal processing and operational tasks, and information transmission in the uplink [3]. Thus, such a low-power equipment can not only communicate with the BS, but also recharge energy storage devices, whose periodic replacement may be costly or even impossible [4], [5].…”

Section: Introductionmentioning

confidence: 99%

“…Although the results in [6]- [10] provide notable insights for WPCN design, they are based on restrictive impractical assumptions. In fact, practical energy harvesting (EH) circuits employ non-linear diodes for signal rectification and thus, the dependence of the harvested power on the input power is highly non-linear in both the low and high input power regimes [4], [11]. If the received RF power at a user device is low, the non-linearity is caused by both the non-linear current-voltage (I-V) characteristic of the rectifying diode [12] and the power-dependent impedance mismatch between the receive antenna and the non-linear rectifier circuit [13].…”

Section: Introductionmentioning

confidence: 99%

Optimal Energy Signal Design for Multiuser MISO WPCNs With Non-Linear Energy Harvesting Circuits

Shanin¹,

Hagelauer²,

Cottatellucci³

et al. 2023

IEEE Trans. Commun.

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The optimal energy signal design for wireless powered communication networks (WPCNs) enabling energysustainable communication for a large number of low-power devices is still an open problem in practical systems. In this work, we study a multi-user WPCN, where a multi-antenna base station (BS) sends an energy signal to multiple single-antenna users, which, in turn, harvest energy from the received signal and utilize it for information transmission in the uplink. In contrast to the existing works on multiple-input single-output (MISO) WPCN design, in this paper, we jointly optimize the energy signal waveform and downlink beamforming at the BS for energy harvesting (EH) devices described by non-linear circuitbased models. To this end, we assume that the BS broadcasts a pulse-modulated signal employing multiple energy signal vectors and we formulate an optimization problem for the joint design of the downlink transmit energy signal vectors, their number, the durations of the transmit pulses, and the time allocation policy for minimization of the average transmit power at the BS. We show that for single-user WPCNs, a single energy signal vector, which is collinear with the maximum ratio transmission (MRT) vector and drives the EH circuit at the user device into saturation, is optimal. Next, for the general multi-user case, we show that the optimal signal design requires a maximum number of energy signal vectors that exceeds the number of users by one and propose an algorithm to obtain the optimal energy signal vectors. Since the complexity of the optimal design is high, we also propose two suboptimal schemes for WPCN design. First, for asymptotic massive WPCNs, where the ratio of the number of users to the number of BS antennas, i.e., the system load, tends to zero, we show that the optimal downlink transmit signal can be obtained in closed-form and comprises a sequence of weighted sums of MRT vectors. Next, based on this result, for general WPCNs with finite system loads, we propose a suboptimal closed-form MRT-based design and a suboptimal semidefinite relaxation (SDR)-based scheme. Our simulation results reveal that the proposed optimal scheme and suboptimal SDR-based design achieve nearly identical performance and outperform two baseline schemes, which are based on linear and sigmoidal EH models. Furthermore, we show that, if the system load of the WPCN is low, the performance gap between the proposed suboptimal solutions is small and becomes negligible as the number of BS antennas tends to infinity.

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Beamforming Design for Self‐Sustainable IRS‐Assisted MISO Downlink Systems

Hu,

2023

Intelligent Surfaces Empowered 6G Wireless Network

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Resource Allocation for Simultaneous Wireless Information and Power Transfer Systems: A Tutorial Overview

Cited by 48 publications

References 120 publications

A Rank-One Optimization Framework and Its Applications to Transmit Beamforming

A Rank-One Optimization Framework and Its Applications to Transmit Beamforming

Optimal Energy Signal Design for Multiuser MISO WPCNs With Non-Linear Energy Harvesting Circuits

Beamforming Design for Self‐Sustainable IRS‐Assisted MISO Downlink Systems

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