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"Link Scheduling in Wireless Powered Internet of Things Networks" by Ying Liu

"Link Scheduling in Wireless Powered Internet of Things Networks" by Ying Liu
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Radio-frequency
Hybrid-access-point
Channel-state-information

"A Novel Hybrid Access Point Channel Access Method for Wireless Powered" by Xiaoyu Song and Kwan Wu Chin

Abstract This paper considers data collection in a wireless powered Internet of Things (IoT) network. Specifically, it addresses the novel problem of determining the mode of each time slot, where a Hybrid Access Point (HAP) needs to decide whether to charge or collect data from devices. Also, in data time slots, the HAP has to decide on a device for data transmission. To this end, we outline an Integer Linear Program (ILP) to determine the mode and the transmitting device over a given planning time horizon. We also propose a rolling horizon approach that uses a Gaussian Mixture Model (GMM) to estimate channel gains. Our results indicate that the amount of data collected by the HAP is affected by its charging power, distance between the HAP and each device, number of devices and planning horizon length. The rolling horizon approach allows the HAP to collect 740% more data as compared to competing approaches.

Integer-linear-program
Hybrid-access-point
Gaussian-mixture-model
முழு-நேரியல்-ப்ரோக்ர்யாம்
கலப்பு-நுழைவு-பாயஂட்

"A Reinforcement Learning Approach to Optimize Energy Usage in RF-Charg" by Honglin Ren and Kwan Wu Chin

Abstract We consider a Radio Frequency (RF)-charging network where sensor devices harvest energy from a solar-powered Hybrid Access Point (HAP) and transmit their data to the HAP. We aim to optimize the power allocation of both the HAP and devices to maximize their Energy Efficiency (EE), which is defined as the total received data (in bits) for each Joule of consumed energy. Unlike prior works, we consider the case where both the HAP and devices have causal knowledge of channel state information and their energy arrival process. We model the power allocation problem as a Two-layer Markov Decision Process (TMDP), where the first layer corresponds to the HAP and the second layer consists of devices. We then outline a novel, decentralized Q-Learning (QL) solution that employs linear function approximation to represent the large state space. The simulation results show that when the HAP and devices employ our solution, their EE is orders of magnitude higher than competing policies.

Energy-efficiency
Radio-frequency
Hybrid-access-point
Two-layer-markov-decision-process
Energy-harvesting
Q-learning
Transmit-power-control
Wireless-sensor-networks
ஆற்றல்-செயல்திறன்
வானொலி-அதிர்வெண்
கலப்பு-நுழைவு-பாயஂட்

"Data Collection in Radio Frequency (RF) Charging Internet of Things Ne" by Hang Yu and Kwan Wu Chin

Abstract This paper considers minimizing the time required to collect L bits from each Radio Frequency (RF)-energy harvesting device serviced by a multi-antenna Hybrid Access Point (HAP). We outline a Mixed Integer Non-Linear Program (MINLP) to determine the transmit power allocation of the HAP and devices over multiple time slots. We also outline a Receding Horizon Control (RHC) approach coupled with a Gaussian Mixture Model (GMM) to optimize the transmit power allocation of the HAP. Our results show that the performance of our approach is within 5% of the optimal solution. Open Access Status

Linear-program
Radio-frequency
Hybrid-access-point
Mixed-integer-non-linear-program
Receding-horizon-control
Gaussian-mixture-model
F-charging
Wireless-power-transfer
நேரியல்-ப்ரோக்ர்யாம்
வானொலி-அதிர்வெண்
கலப்பு-நுழைவு-பாயஂட்