Deep Reinforcement-Learning Based Power and Beamforming Coordination for IoT Underlying 5G Networks

Chiapin Wang; Cheng Lin Hsieh; Han Chi Gao

doi:10.1109/ICEIB53692.2021.9686442

Deep Reinforcement-Learning Based Power and Beamforming Coordination for IoT Underlying 5G Networks

Chiapin Wang^*, Cheng Lin Hsieh, Han Chi Gao

^*此作品的通信作者

電機工程學系

研究成果: 書貢獻/報告類型 › 會議論文篇章

1 引文斯高帕斯（Scopus）

摘要

When the Internet of Things (IoT) is employed for Device-to-Device (D2D) communications that underlies 5G cellular networks, cross-tier interferences is introduced without suitable coordination between cellular users and D2D communications. In this study, a power and beamforming coordination method is proposed based on deep reinforcement learning to reduce cross-tier interferences between cellular users and D2D communications to increase the overall network utility. Results from simulation show that the proposed scheme efficiently increases the total system utility for IoT underlying 5G cellular networks when compared with other existing schemes.

原文	英語
主出版物標題	2021 IEEE International Conference on Electronic Communications, Internet of Things and Big Data, ICEIB 2021
編輯	Teen-Hang Meen
發行者	Institute of Electrical and Electronics Engineers Inc.
頁面	20-22
頁數	3
ISBN（電子）	9781665437554
DOIs	https://doi.org/10.1109/ICEIB53692.2021.9686442
出版狀態	已發佈 - 2021
事件	2021 IEEE International Conference on Electronic Communications, Internet of Things and Big Data, ICEIB 2021 - Yilan, 臺灣持續時間: 2021 12月 10 → 2021 12月 12

出版系列

名字	2021 IEEE International Conference on Electronic Communications, Internet of Things and Big Data, ICEIB 2021

會議

會議	2021 IEEE International Conference on Electronic Communications, Internet of Things and Big Data, ICEIB 2021
國家/地區	臺灣
城市	Yilan
期間	2021/12/10 → 2021/12/12

ASJC Scopus subject areas

電腦網路與通信
電腦科學應用
硬體和架構
資訊系統
資訊系統與管理
安全、風險、可靠性和品質

存取文件

10.1109/ICEIB53692.2021.9686442

其它文件與鏈接

Link to publication in Scopus

引用此

Wang, C., Hsieh, C. L., & Gao, H. C. (2021). Deep Reinforcement-Learning Based Power and Beamforming Coordination for IoT Underlying 5G Networks. 於 T.-H. Meen (編輯), 2021 IEEE International Conference on Electronic Communications, Internet of Things and Big Data, ICEIB 2021 (頁 20-22). (2021 IEEE International Conference on Electronic Communications, Internet of Things and Big Data, ICEIB 2021). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICEIB53692.2021.9686442

Deep Reinforcement-Learning Based Power and Beamforming Coordination for IoT Underlying 5G Networks. / Wang, Chiapin; Hsieh, Cheng Lin; Gao, Han Chi.
2021 IEEE International Conference on Electronic Communications, Internet of Things and Big Data, ICEIB 2021. 編輯 / Teen-Hang Meen. Institute of Electrical and Electronics Engineers Inc., 2021. p. 20-22 (2021 IEEE International Conference on Electronic Communications, Internet of Things and Big Data, ICEIB 2021).

研究成果: 書貢獻/報告類型 › 會議論文篇章

Wang, C, Hsieh, CL & Gao, HC 2021, Deep Reinforcement-Learning Based Power and Beamforming Coordination for IoT Underlying 5G Networks. 於 T-H Meen (編輯), 2021 IEEE International Conference on Electronic Communications, Internet of Things and Big Data, ICEIB 2021. 2021 IEEE International Conference on Electronic Communications, Internet of Things and Big Data, ICEIB 2021, Institute of Electrical and Electronics Engineers Inc., 頁 20-22, 2021 IEEE International Conference on Electronic Communications, Internet of Things and Big Data, ICEIB 2021, Yilan, 臺灣, 2021/12/10. https://doi.org/10.1109/ICEIB53692.2021.9686442

Wang C, Hsieh CL, Gao HC. Deep Reinforcement-Learning Based Power and Beamforming Coordination for IoT Underlying 5G Networks. 於 Meen TH, 編輯, 2021 IEEE International Conference on Electronic Communications, Internet of Things and Big Data, ICEIB 2021. Institute of Electrical and Electronics Engineers Inc. 2021. p. 20-22. (2021 IEEE International Conference on Electronic Communications, Internet of Things and Big Data, ICEIB 2021). doi: 10.1109/ICEIB53692.2021.9686442

Wang, Chiapin ; Hsieh, Cheng Lin ; Gao, Han Chi. / Deep Reinforcement-Learning Based Power and Beamforming Coordination for IoT Underlying 5G Networks. 2021 IEEE International Conference on Electronic Communications, Internet of Things and Big Data, ICEIB 2021. 編輯 / Teen-Hang Meen. Institute of Electrical and Electronics Engineers Inc., 2021. 頁 20-22 (2021 IEEE International Conference on Electronic Communications, Internet of Things and Big Data, ICEIB 2021).

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title = "Deep Reinforcement-Learning Based Power and Beamforming Coordination for IoT Underlying 5G Networks",

abstract = "When the Internet of Things (IoT) is employed for Device-to-Device (D2D) communications that underlies 5G cellular networks, cross-tier interferences is introduced without suitable coordination between cellular users and D2D communications. In this study, a power and beamforming coordination method is proposed based on deep reinforcement learning to reduce cross-tier interferences between cellular users and D2D communications to increase the overall network utility. Results from simulation show that the proposed scheme efficiently increases the total system utility for IoT underlying 5G cellular networks when compared with other existing schemes.",

keywords = "5G cellular networks, Beamforming, Deep Reinforcement learning, Device-to-Device (D2D) communication, Internet of Things (IoT), Power control",

author = "Chiapin Wang and Hsieh, {Cheng Lin} and Gao, {Han Chi}",

note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 2021 IEEE International Conference on Electronic Communications, Internet of Things and Big Data, ICEIB 2021 ; Conference date: 10-12-2021 Through 12-12-2021",

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AU - Wang, Chiapin

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Y1 - 2021

N2 - When the Internet of Things (IoT) is employed for Device-to-Device (D2D) communications that underlies 5G cellular networks, cross-tier interferences is introduced without suitable coordination between cellular users and D2D communications. In this study, a power and beamforming coordination method is proposed based on deep reinforcement learning to reduce cross-tier interferences between cellular users and D2D communications to increase the overall network utility. Results from simulation show that the proposed scheme efficiently increases the total system utility for IoT underlying 5G cellular networks when compared with other existing schemes.

AB - When the Internet of Things (IoT) is employed for Device-to-Device (D2D) communications that underlies 5G cellular networks, cross-tier interferences is introduced without suitable coordination between cellular users and D2D communications. In this study, a power and beamforming coordination method is proposed based on deep reinforcement learning to reduce cross-tier interferences between cellular users and D2D communications to increase the overall network utility. Results from simulation show that the proposed scheme efficiently increases the total system utility for IoT underlying 5G cellular networks when compared with other existing schemes.

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KW - Beamforming

KW - Deep Reinforcement learning

KW - Device-to-Device (D2D) communication

KW - Internet of Things (IoT)

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Deep Reinforcement-Learning Based Power and Beamforming Coordination for IoT Underlying 5G Networks

摘要

出版系列

會議

ASJC Scopus subject areas

存取文件

其它文件與鏈接

指紋

引用此