Charging Unplugged: Will Distributed Laser Charging for Mobile Wireless Power Transfer Work?

IEEE Trans. Veh. Technol.

Chen

et al. 2019

Self Cite

290

192

A novel framework is proposed for the trajectory design of multiple unmanned aerial vehicles (UAVs) based on the prediction of users' mobility information. The problem of joint trajectory design and power control is formulated for maximizing the instantaneous sum transmit rate while satisfying the rate requirement of users. In an effort to solve this pertinent problem, a three-step approach is proposed which is based on machine learning techniques to obtain both the position information of users and the trajectory design of UAVs. Firstly, a multi-agent Qlearning based placement algorithm is proposed for determining the optimal positions of the UAVs based on the initial location of the users. Secondly, in an effort to determine the mobility information of users based on a real dataset, their position data is collected from Twitter to describe the anonymous usertrajectories in the physical world. In the meantime, an echo state network (ESN) based prediction algorithm is proposed for predicting the future positions of users based on the real dataset. Thirdly, a multi-agent Q-learning based algorithm is conceived for predicting the position of UAVs in each time slot based on the movement of users. In this algorithm, multiple UAVs act as agents to find optimal actions by interacting with their environment and learn from their mistakes. Additionally, we also prove that the proposed multi-agent Q-learning based trajectory design and power control algorithm can converge under mild conditions. Numerical results are provided to demonstrate that as the size of the reservoir increases, the proposed ESN approach improves the prediction accuracy. Finally, we demonstrate that throughput gains of about 17% are achieved.

Section: System Modelmentioning

confidence: 99%

Trajectory Design and Power Control for Multi-UAV Assisted Wireless Networks: A Machine Learning Approach

IEEE Trans. Veh. Technol.

Chen

et al. 2019

Self Cite

290

192

“…Multiple IoT devices can be charged simultaneously in the RBC system while guaranteeing the high-power, longdistance, mobile and safe charging [9]. A Point-to-Point (PTP) RBC system consists of a RBC transmitter and a RBC receiver, of which the structure is presented in Fig.…”

Section: A Point-to-point Rbc Systemmentioning

confidence: 99%

Earning Maximization With Quality of Charging Service Guarantee for IoT Devices

Wen

Zhang

IEEE Internet Things J.

et al. 2019

Self Cite

Resonant Beam Charging (RBC) is a promising Wireless Power Transfer (WPT) technology to provide longrange, high-power, mobile and safe wireless power for the Internet of Things (IoT) devices. The Point-to-Multipoint (PtMP) RBC system can charge multiple receivers simultaneously similar to WiFi communications. To guarantee the Quality of Charging Service (QoCS) for each receiver and maximize the overall earning in the PtMP RBC service, we specify the Charging Pricing Strategy (CPS) and develop the High Priority Charge (HPC) scheduling algorithm to control the charging order and power allocation. Each receiver is assigned a priority, which is updated dynamically based on its State of Charging (SOC) and specified charging power. The receivers with high priorities are scheduled to be charged in each time slot. We present the pseudo code of the HPC algorithm based on quantifying the receiver's SOC, discharging energy and various relevant parameters. Relying on simulation analysis, we demonstrate that the HPC algorithm can achieve better QoCS and earning than the Round-Robin Charge (RRC) scheduling algorithm. Based on the performance evaluation, we illustrate that the methods to improve the PtMP RBC service are: 1) limiting the receiver number within a reasonable range and 2) prolonging the charging duration as long as possible. In summary, the HPC scheduling algorithm provides a practical strategy to maximize the earning of the PtMP RBC service with each receiver's QoCS guarantee.

“…Resonant beam charging (RBC), as known as distributed laser charging (DLC) [14], can transfer energy via a resonant beam between the transmitter and the receiver. Fig.…”

Section: Introductionmentioning

confidence: 99%

TDMA in Adaptive Resonant Beam Charging for IoT Devices

Xiong

IEEE Internet Things J.

Zhang

et al. 2019

Self Cite

Resonant beam charging (RBC) can realize wireless power transfer (WPT) from a transmitter to multiple Internet of things (IoT) devices via resonant beams. The adaptive RBC (ARBC) can effectively improve its energy utilization. In order to support multi-user WPT in the ARBC system, we propose the time-division multiple access (TDMA) method and design the TDMA-based WPT scheduling algorithm. Our TDMA WPT method has the features of concurrently charging, continuous charging current, individual user power control, constant driving power and flexible driving power control. The simulation shows that the TDMA scheduling algorithm has high efficiency, as the total charging time is roughly half (46.9% when charging 50 receivers) of that of the alternative scheduling algorithm. Furthermore, the TDMA for WPT inspires the ideas of enhancing the ARBC system, such as flow control and quality of service (QoS).