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Multi-Agent Q-Learning Based Multi-UAV Wireless Networks for Maximizing Energy Efficiency: Deployment and Power Control Strategy Design

Authors
Lee, S.Yu, H.Lee, H.
Issue Date
5월-2022
Publisher
Institute of Electrical and Electronics Engineers Inc.
Keywords
Air-to-Ground Channel; Energy Efficiency Maximization.; Heuristic algorithms; Internet of Things; Multi-Agent Distributed Q-Learning; Optimization; Power Control; Power control; Throughput; Unmanned Aerial Vehicle-Base Station; Unmanned aerial vehicles; Wireless networks
Citation
IEEE Internet of Things Journal, v.9, no.9, pp.6434 - 6442
Indexed
SCIE
SCOPUS
Journal Title
IEEE Internet of Things Journal
Volume
9
Number
9
Start Page
6434
End Page
6442
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/140527
DOI
10.1109/JIOT.2021.3113128
ISSN
2327-4662
Abstract
In air-to-ground communications, the network lifetime depends on the operation time of unmanned aerial vehicle-base stations (UAV-BSs) owing to the restricted battery capacity. Therefore, the maximization of energy efficiency and the minimization of outage ground users are important metrics of network performance. To achieve these two objectives, the location and transmit power of the UAV-BSs in the network must be optimized. This optimization problem may not be tractable in the conventional optimization framework because multiple UAV-BSs interact in a complicated manner. Hence, we formulate the problem as a Markov decision process and develop an algorithm to obtain a solution in a reinforcement learning framework. To avoid a central controller and high computational complexity, we employ a multi-agent distributed Q-learning algorithm to obtain a solution. Specifically, we propose a multi-agent Q-learning-based UAV-BS deployment and power control strategy to maximize energy efficiency and minimize the number of outage users in multi-UAV wireless networks. Through intensive simulations, it is demonstrated that the proposed algorithm can outperform benchmark algorithms in terms of average energy efficiency and number of average outage users in multi-UAV wireless networks. IEEE
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