Distributed Deep Reinforcement Learning-Based Energy Efficiency Maximization in 3D Cellular Networks

Seungmin Lee; Tae-Won Ban; Howon Lee

doi:10.7840/kics.2023.48.8.942

Distributed Deep Reinforcement Learning-Based Energy Efficiency Maximization in 3D Cellular Networks

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3차원 셀룰러 네트워크기법에서 분산 심층강화학습 기반 에너지 효율 최대화

Type: Academic journal

Author: Seungmin Lee (한경국립대학교) Tae-Won Ban (경상국립대학교) Howon Lee (한경국립대학교)

Journal: Korea Institute Of Communication Sciences The Journal of Korean Institute of Communications and Information Sciences Vol.48 No.8 KCI Accredited Journals SCOPUS

Published: 2023.8

Pages: 942 - 949 (8page)

DOI: 10.7840/kics.2023.48.8.942

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Distributed Deep Reinforcement Learning-Based Energy Efficiency Maximization in 3D Cellular Networks

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2023

Distributed Deep Reinforcement Learning-Based Energy Efficiency Maximization in 3D Cellular Networks

Seungmin Lee , Tae-Won Ban , Howon Lee The Journal of Korean Institute of Communications and Information Sciences 2023.08 Academic journal

2022

Distributed Deep Reinforcement Learning-Based Energy Efficiency Maximization in 3D Cellular Networks Considering User Mobility

Seungmin Lee , Tae-Won ban , Howon Lee Proceedings of Symposium of the Korean Institute of communications and Information Sciences 2022.06 Proceeding

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In this paper, we consider the multiple unmanned aerial vehicle-base station(UBS)-based 3D cellular networks to provide air-to-ground(A2G) communication coverage to moving ground users. Especially, to alleviate the short network lifetime problem of the UBS networks, we aim to control the movement and the transmission power of UBS so that maximizing the network-wide energy efficiency. However, considering the dynamic environment in which ground users move, deriving the optimal solution to the problem is significantly difficult with existing iterative methods or optimization methods. Therefore, in this paper, we propose a distributed deep Q-network(DQN)-based UBS control method. Also, to show the advantages of the distributed learning, we introduce two centralized learning methods, and then we consider the two centralized learning method, multi-agent distributed Q-learning(MD-QL) and greedy action(GA) methods as benchmarks. Conclusionally, we verify that the performance of the proposed method outperforms the conventional methods according to the movement speed of the ground user and the number of UBSs.

#무인항공기 #심층 큐-네트워크 #UAV 제어 #공중-지상 채널 #에너지 효율 극대화 #UAV #Deep Q-Network #UAV Control #Air-to-Ground Channel #Energy Efficiency Maximization

요약
ABSTRACT
Ⅰ. 서론
Ⅱ. 시스템 및 채널 모델
Ⅲ. 분산 DQN 기반 UBS 제어 방안
Ⅳ. 실험 결과 및 분석
Ⅴ. 결론
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Samsung, 6G The Next Hyper-Connected Exp erience for ALL, Samsung’s 6G White Paper , 2020. (https://news.samsung.com/global/samsungs-6g-white-paper-lays-out-the-companys-vision-for-the-next-generation-of-communications-technology).

5G Forum, 6G Technology Trends, in 6G Working Group White Paper , 2021. (http://6gglobal.org/kr/sub/publication/publication_view.php?idx=2)

H. Lee, B. Lee, H. Yang, J. Kim, S. Kim, W. Shin, B. Shim, and H. V. Poor, “Towards 6g hyper-connectivity: Vision, challenges, and key enabling technologies,” IEEE J. Commun. and Netw. (to appear) (https://doi.org/10.23919/JCN.2023.000006)

H. Yu, H. Lee, and H. B. Jeon, “What is 5g? emerging 5g mobile services and network requirements,” Sustainability 2017, vol. 9, no. 10, pp. 1-22, Oct. 2017. (https://doi.org/10.3390/su9101848).

S. Lim, H. Yu, and H. Lee, “Optimal Tethered-UAV deployment in a2g communication networks: Multi-agent q-learning approach,” IEEE Internet of Things J., vol. 9, no. 19, pp. 18539-18549, Oct. 2022. (https://doi.org/10.1109/JIOT.2022.3161260).

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