A Multi-Armed Bandit Approach to Wireless Information and Power Transfer

Xing, Yuan; Qian, Yuchen; Dong, Liang

doi:10.1109/lcomm.2020.2969658

Cited by 7 publications

(4 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The MAB problem or bandit problem is one of the general problems first discussed by Robbins in [ 35 ]. In the MAB problem, the player selects a slot machine to play among several slot machines, aiming to maximize the number of coins he/she can earn by repeatedly playing [ 36 ]. The player needs to learn the probability of the number of coins for each slot machine to find the slot machine that pays the most by repeatedly playing.…”

Section: Channel and Sf Selection Based On Mab Algorithmsmentioning

confidence: 99%

Combinatorial MAB-Based Joint Channel and Spreading Factor Selection for LoRa Devices

Urabe

Fujisawa

et al. 2023

Sensors

View full text Add to dashboard Cite

Long-Range (LoRa) devices have been deployed in many Internet of Things (IoT) applications due to their ability to communicate over long distances with low power consumption. The scalability and communication performance of the LoRa systems are highly dependent on the spreading factor (SF) and channel allocations. In particular, it is important to set the SF appropriately according to the distance between the LoRa device and the gateway since the signal reception sensitivity and bit rate depend on the used SF, which are in a trade-off relationship. In addition, considering the surge in the number of LoRa devices recently, the scalability of LoRa systems is also greatly affected by the channels that the LoRa devices use for communications. It was demonstrated that the lightweight decentralized learning-based joint channel and SF-selection methods can make appropriate decisions with low computational complexity and power consumption in our previous study. However, the effect of the location situation of the LoRa devices on the communication performance in a practical larger-scale LoRa system has not been studied. Hence, to clarify the effect of the location situation of the LoRa devices on the communication performance in LoRa systems, in this paper, we implemented and evaluated the learning-based joint channel and SF-selection methods in a practical LoRa system. In the learning-based methods, the channel and SF are decided only based on the ACKnowledge information. The learning methods evaluated in this paper were the Tug of War dynamics, Upper Confidence Bound 1, and ϵ-greedy algorithms. Moreover, to consider the relevance of the channel and SF, we propose a combinational multi-armed bandit-based joint channel and SF-selection method. Compared with the independent methods, the combinations of the channel and SF are set as arms. Conversely, the SF and channel are set as independent arms in the independent methods that are evaluated in our previous work. From the experimental results, we can see the following points. First, the combinatorial methods can achieve a higher frame success rate and fairness than the independent methods. In addition, the FSR can be improved by joint channel and SF selection compared to SF selection only. Moreover, the channel and SF selection dependents on the location situation to a great extent.

show abstract

Section: Channel and Sf Selection Based On Mab Algorithmsmentioning

confidence: 99%

Combinatorial MAB-Based Joint Channel and Spreading Factor Selection for LoRa Devices

Urabe

Fujisawa

et al. 2023

Sensors

View full text Add to dashboard Cite

show abstract

“…the best action as a benchmark which might not exist in some rounds [61]. • Combinatorial Bandits: It is a multi-variable bandit game where the player selects values from a group of variables [62], not from a single one. The number of probable selections per iteration is exponential in the number of discrete variables.…”

Section: ) Multi-armed Bandits (Mabs)mentioning

confidence: 99%

AI-Enabled UAV Communications: Challenges and Future Directions

et al. 2022

View full text Add to dashboard Cite

Recently, unmanned aerial vehicles (UAVs) communications gained significant concentration as a talented technology for future wireless communications using its remarkable advantages and broad applicability. Furthermore, UAV networks' high complex configurations and designs encourage researchers to leverage relevant artificial intelligence (AI) techniques for better beyond fifth-generation (B5G)/sixth-generation (6G) services. This article summarizes AI-aided UAV solutions designated for forthcoming wireless networks. Besides, we deliver a comprehensive summary of machine learning (ML) approaches, including their applications and valuable contributions towards effective UAV network implementations, particularly advanced ML ones like bandits, federated learning (FL), meta-learning, etc. Finally, detailed UAV communication-related future research scopes and challenges is highlighted.INDEX TERMS Unmanned aerial vehicles (UAVs), artificial intelligence (AI), deep learning (DL), metalearning, federated learning (FL), and reinforcement learning (RL).

show abstract

“…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

Self Cite

View full text Add to dashboard Cite

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.

show abstract

A Multi-Armed Bandit Approach to Wireless Information and Power Transfer

Cited by 7 publications

References 6 publications

Combinatorial MAB-Based Joint Channel and Spreading Factor Selection for LoRa Devices

Combinatorial MAB-Based Joint Channel and Spreading Factor Selection for LoRa Devices

AI-Enabled UAV Communications: Challenges and Future Directions

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

Contact Info

Product

Resources

About