Supplying the Power Requirements to a Sensor Network Using Radio Frequency Power Transfer

Percy, Steven; Knight, Chris; Cooray, F. R.; Smart, Ken

doi:10.3390/s120708571

Cited by 31 publications

(22 citation statements)

References 10 publications

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“…The implemented sensor transmitter and receiver consume the power of 1.79mW and 0.683mW, respectively, to achieve the data rate of 500kbps. Similar system designs with dedicated wireless charger have been reported in [144]- [146] for sensors with batteries and [147], [148] for batteryless sensors. Instead of relying on dedicated wireless charger, wireless charging systems based on ambient energy harvesting have also been developed.…”

Section: B Applications Of Wireless Chargingmentioning

confidence: 70%

Wireless Charging Technologies: Fundamentals, Standards, and Network Applications

Wang

Niyato

et al. 2016

IEEE Commun. Surv. Tutorials

839

395

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Abstract-Wireless charging is a technology of transmitting power through an air gap to electrical devices for the purpose of energy replenishment. The recent progress in wireless charging techniques and development of commercial products have provided a promising alternative way to address the energy bottleneck of conventionally portable battery-powered devices. However, the incorporation of wireless charging into the existing wireless communication systems also brings along a series of challenging issues with regard to implementation, scheduling, and power management. In this article, we present a comprehensive overview of wireless charging techniques, the developments in technical standards, and their recent advances in network applications. In particular, with regard to network applications, we review the static charger scheduling strategies, mobile charger dispatch strategies and wireless charger deployment strategies. Additionally, we discuss open issues and challenges in implementing wireless charging technologies. Finally, we envision some practical future network applications of wireless charging.

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Section: B Applications Of Wireless Chargingmentioning

confidence: 70%

Wireless Charging Technologies: Fundamentals, Standards, and Network Applications

Wang

Niyato

et al. 2016

IEEE Commun. Surv. Tutorials

839

395

View full text Add to dashboard Cite

show abstract

“…Among the three variables P s,i , P r,i , and δ i , we alternatively fix two variables and determine the third variable by solving a single-variable optimization problem. Then, we update the adjustable gain {u i } and the weight {w i } for the obtained (P s,i , P r,i , δ i ) in the previous iteration according to (48) and (49), respectively. The aforementioned procedure is repeated until a convergent point is reached.…”

Section: Alternating Optimization Problem: Weighted Sum-mse Minimimentioning

confidence: 99%

“…In this section, we demonstrate the sum rate performance of the proposed power control and transfer algorithm and validate the theoretical findings in Section IV by computer simulation. The path loss models for the data transfer and the dedicated power transfer channels are both described as 25.17 + 20 × log 10 (d) dB (d : distance in kilometer) [48], [49]. The data channel bandwidth and the thermal noise power density are respectively given as 1 MHz and −174 dBm/Hz.…”

Section: A Simulation Settingsmentioning

confidence: 99%

Toward Optimal Power Control and Transfer for Energy Harvesting Amplify-and-Forward Relay Networks

Singh

Lin

et al. 2018

IEEE Trans. Wireless Commun.

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In this paper, we study an amplify-and-forward (AF) relay network with energy harvesting (EH) source and relay nodes. Both nodes can continuously harvest energy from the environment and store it in batteries with finite capacity. Additionally, the source node is capable of transferring a portion of its energy to the relay node through a dedicated channel. The network performance depends on not only the energy arrival profiles at EH nodes but also the energy cooperation between them. We jointly design power control and transfer for maximizing the sum rate over finite time duration, subject to energy causality and battery storage constraints. By introducing auxiliary variables to confine the accumulated power expenditure, this non-convex problem is solved via a successive convex approximation (SCA) approach, and the local optimum solutions are obtained through dual decomposition. Also when channels are quasi-static and the power control values of the source (relay) node are preset to a constant, a monotonically increasing power control structure with the time is revealed for the relay (source) node with infinite battery capacity. Computer simulations are used to validate the theoretical findings and to quantify the impact of various factors such as EH intensity at nodes and relay position on the sum rate performance.

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“…Additionally, researchers at the University of Washington have deployed an energy scavenging WiFi camera [3]. Specially, the transmitter can work as an energy source, as in the dedicated RF charging [4], [5]. Thus, both energy and information can be delivered to the receiver via RF waves [6], [7].…”

Section: Introductionmentioning

confidence: 99%

Performance of Energy Harvesting Receivers With Power Optimization

Motani

2018

IEEE Trans. Commun.

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The difficulty of modeling energy consumption in communication systems leads to challenges in energy harvesting (EH) systems, in which nodes scavenge energy from their environment. An EH receiver must harvest enough energy for demodulating and decoding. The energy required depends upon factors, like code rate and signal-to-noise ratio, which can be adjusted dynamically. We consider a receiver which harvests energy from ambient sources and the transmitter, meaning the received signal is used for both EH and information decoding. Assuming a generalized function for energy consumption, we maximize the total number of information bits decoded, under both average and peak power constraints at the transmitter, by carefully optimizing the power used for EH, power used for information transmission, fraction of time for EH, and code rate. For transmission over a single block, we find there exist problem parameters for which either maximizing power for information transmission or maximizing power for EH is optimal. In the general case, the optimal solution is a tradeoff of the two. For transmission over multiple blocks, we give an upper bound on performance and give sufficient and necessary conditions to achieve this bound. Finally, we give some numerical results to illustrate our results and analysis. Index TermsEnergy harvesting communication systems, Simultaneous energy and information transfer, Timeswitching, Joint power and rate optimization

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Supplying the Power Requirements to a Sensor Network Using Radio Frequency Power Transfer

Cited by 31 publications

References 10 publications

Wireless Charging Technologies: Fundamentals, Standards, and Network Applications

Wireless Charging Technologies: Fundamentals, Standards, and Network Applications

Toward Optimal Power Control and Transfer for Energy Harvesting Amplify-and-Forward Relay Networks

Performance of Energy Harvesting Receivers With Power Optimization

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