Passive Radio-Frequency Energy Harvesting through Wireless Information Transmission

Xing, Yuan; Dong, Liang

doi:10.1109/dcoss.2017.33

Cited by 6 publications

(4 citation statements)

References 9 publications

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“…As shown in Figure 1, an information transmitter communicates with its receiver while perceived by K nearby RF energy harvesters [8]. Both the transmitter and the receiver are equipped with M antennas, while each RF energy harvester is equipped with one receive antenna.…”

Section: System Modelmentioning

confidence: 99%

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Optimal Wireless Information and Power Transfer Using Deep Q-Network

Xing

Pan²,

Xu³

et al. 2021

Wireless Power Transfer

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In this paper, a multiantenna wireless transmitter communicates with an information receiver while radiating RF energy to surrounding energy harvesters. The channel between the transceivers is known to the transmitter, but the channels between the transmitter and the energy harvesters are unknown to the transmitter. By designing its transmit covariance matrix, the transmitter fully charges the energy buffers of all energy harvesters in the shortest amount of time while maintaining the target information rate toward the receiver. At the beginning of each time slot, the transmitter determines the particular beam pattern to transmit with. Throughout the whole charging process, the transmitter does not estimate the energy harvesting channel vectors. Due to the high complexity of the system, we propose a novel deep Q-network algorithm to determine the optimal transmission strategy for complex systems. Simulation results show that deep Q-network is superior to the existing algorithms in terms of the time consumption to fulfill the wireless charging process.

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Section: System Modelmentioning

confidence: 99%

“…Most of the existing works assume the channel state information (CSI) is completely known. Given the complete CSI, the transmitter designs the transmit covariance matrix to achieve the maximum information rate while satisfying the RF energy harvesting requirement [4,8].…”

Section: Introductionmentioning

confidence: 99%

Optimal Wireless Information and Power Transfer Using Deep Q-Network

Xing

Pan²,

Xu³

et al. 2021

Wireless Power Transfer

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“…In near-field wireless power transfer, the IoT devices, which are charged by resonant inductive coupling, have to be placed very close to the wireless transmitters (less than 5 cm) [2]. In far-field wireless power transfer, the IoT devices use the electromagnetic waves from transmitters as the power resource and the effective charging distance ranges from 50 centimeters to 1.5 meters [3][4][5]. Compared to the near-field transmitters, the far-field wireless power transmitters can charge the IoT devices (including the mobile IoT devices) that are deployed in a larger space.…”

Section: Introductionmentioning

confidence: 99%

Optimal Path Planning for Wireless Power Transfer Robot Using Area Division Deep Reinforcement Learning

Xing

Young

Nguyen

et al. 2022

Wireless Power Transfer

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This paper aims to solve the optimization problems in far-field wireless power transfer systems using deep reinforcement learning techniques. The Radio-Frequency (RF) wireless transmitter is mounted on a mobile robot, which patrols near the harvested energy-enabled Internet of Things (IoT) devices. The wireless transmitter intends to continuously cruise on the designated path in order to fairly charge all the stationary IoT devices in the shortest time. The Deep Q-Network (DQN) algorithm is applied to determine the optimal path for the robot to cruise on. When the number of IoT devices increases, the traditional DQN cannot converge to a closed-loop path or achieve the maximum reward. In order to solve these problems, an area division Deep Q-Network (AD-DQN) is invented. The algorithm can intelligently divide the complete charging field into several areas. In each area, the DQN algorithm is utilized to calculate the optimal path. After that, the segmented paths are combined to create a closed-loop path for the robot to cruise on, which can enable the robot to continuously charge all the IoT devices in the shortest time. The numerical results prove the superiority of the AD-DQN in optimizing the proposed wireless power transfer system.

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“…In another K-user MIMO scenario, the total transmit power of the users is minimized by jointly designing transmit beamformers, power splitters, and receive filters [27]. When a multi-antenna transmitter communicates with its information receiver, it can intentionally focus the transmitted power to the nearby RF energy harvesters with a guarantee on its information rate [28].…”

Section: Introductionmentioning

confidence: 99%

Transmission Game in MIMO Interference Channels With Radio-Frequency Energy Harvesting

Dong

2017

Preprint

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For multi-user transmissions over MIMO interference channels, each user designs the transmit covariance matrix to maximize its information rate. When passive radiofrequency (RF) energy harvesters are present in the network, the transmissions are constrained by both the transmit power limits and the energy harvesting requirements. A passive RF energy harvester collects the radiated energy from nearby wireless information transmitters instead of using a dedicated wireless power source. It needs multiple transmitters to concentrate their RF radiation on it because typical electric field strengths are weak. In this paper, strategic games are proposed for the multi-user transmissions. First, in a non-cooperative game, each transmitter has a best-response strategy for the transmit covariance matrix that follows a multi-level water-filling solution. A pure-strategy Nash equilibrium exists. Secondly, in a cooperative game, there is no need to estimate the proportion of the harvested energy from each transmitter. Rather, the transmitters bargain over the unit-reward of the energy contribution. An approximation of the information rate is used in constructing the individual utility such that the problem of network utility maximization can be decomposed and the bargaining process can be implemented distributively. The bargaining solution gives a point of rates that is superior to the Nash equilibria and close to the Pareto front. Simulation results verify the algorithms that provide good communication performance while satisfying the RF energyharvesting requirements.

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Passive Radio-Frequency Energy Harvesting through Wireless Information Transmission

Cited by 6 publications

References 9 publications

Optimal Wireless Information and Power Transfer Using Deep Q-Network

Optimal Wireless Information and Power Transfer Using Deep Q-Network

Optimal Path Planning for Wireless Power Transfer Robot Using Area Division Deep Reinforcement Learning

Transmission Game in MIMO Interference Channels With Radio-Frequency Energy Harvesting

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