Federated Deep Learning for RIS-assisted UAV-enabled Wireless Communications

Heejae Park; Tri Hai Nguyen; Laihyuk Park

doi:10.1109/ICTC55196.2022.9952406

Federated Deep Learning for RIS-assisted UAV-enabled Wireless Communications

Heejae Park, Tri Hai Nguyen, Laihyuk Park

Dept. of Computer Science and Engineering

Seoul National University of Science and Technology (SNUST)

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

11 Scopus citations

Abstract

Unmanned aerial vehicles (UAVs) are becoming more crucial to enhancing spectral efficiency in the 6G network. However, there are problems such as blockage and energy-hungry. To solve these problems, an UAV-assisted reconfigurable intelligent surface (RIS) communication has been proposed for wireless system. RIS is a two-dimensional meta-surface, that is composed of cost-effective and energy-efficient passive reflecting elements, which can enhance spectral and energy efficiency. This paper formulates the problem of maximizing the achievable data rate by using long short-term memory and federated learning. We proposed the method that consists of two stages: 1) training stage for a global model in UAV, and 2) resource allocation stage in RIS controller.

Original language	English
Title of host publication	ICTC 2022 - 13th International Conference on Information and Communication Technology Convergence
Subtitle of host publication	Accelerating Digital Transformation with ICT Innovation
Publisher	IEEE Computer Society
Pages	831-833
Number of pages	3
ISBN (Electronic)	9781665499392
DOIs	https://doi.org/10.1109/ICTC55196.2022.9952406
State	Published - 2022
Event	13th International Conference on Information and Communication Technology Convergence, ICTC 2022 - Jeju Island, Korea, Republic of Duration: 19 Oct 2022 → 21 Oct 2022

Publication series

Name	International Conference on ICT Convergence
Volume	2022-October
ISSN (Print)	2162-1233
ISSN (Electronic)	2162-1241

Conference

Conference	13th International Conference on Information and Communication Technology Convergence, ICTC 2022
Country/Territory	Korea, Republic of
City	Jeju Island
Period	19/10/22 → 21/10/22

Keywords

optimization
re-source allocation
RIS
UAV communication

Access to Document

10.1109/ICTC55196.2022.9952406

Cite this

Park, H., Nguyen, T. H., & Park, L. (2022). Federated Deep Learning for RIS-assisted UAV-enabled Wireless Communications. In ICTC 2022 - 13th International Conference on Information and Communication Technology Convergence: Accelerating Digital Transformation with ICT Innovation (pp. 831-833). (International Conference on ICT Convergence; Vol. 2022-October). IEEE Computer Society. https://doi.org/10.1109/ICTC55196.2022.9952406

Park, Heejae ; Nguyen, Tri Hai ; Park, Laihyuk. / Federated Deep Learning for RIS-assisted UAV-enabled Wireless Communications. ICTC 2022 - 13th International Conference on Information and Communication Technology Convergence: Accelerating Digital Transformation with ICT Innovation. IEEE Computer Society, 2022. pp. 831-833 (International Conference on ICT Convergence).

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title = "Federated Deep Learning for RIS-assisted UAV-enabled Wireless Communications",

abstract = "Unmanned aerial vehicles (UAVs) are becoming more crucial to enhancing spectral efficiency in the 6G network. However, there are problems such as blockage and energy-hungry. To solve these problems, an UAV-assisted reconfigurable intelligent surface (RIS) communication has been proposed for wireless system. RIS is a two-dimensional meta-surface, that is composed of cost-effective and energy-efficient passive reflecting elements, which can enhance spectral and energy efficiency. This paper formulates the problem of maximizing the achievable data rate by using long short-term memory and federated learning. We proposed the method that consists of two stages: 1) training stage for a global model in UAV, and 2) resource allocation stage in RIS controller.",

keywords = "optimization, re-source allocation, RIS, UAV communication",

author = "Heejae Park and Nguyen, \{Tri Hai\} and Laihyuk Park",

note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 13th International Conference on Information and Communication Technology Convergence, ICTC 2022 ; Conference date: 19-10-2022 Through 21-10-2022",

year = "2022",

doi = "10.1109/ICTC55196.2022.9952406",

language = "English",

series = "International Conference on ICT Convergence",

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booktitle = "ICTC 2022 - 13th International Conference on Information and Communication Technology Convergence",

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Park, H, Nguyen, TH & Park, L 2022, Federated Deep Learning for RIS-assisted UAV-enabled Wireless Communications. in ICTC 2022 - 13th International Conference on Information and Communication Technology Convergence: Accelerating Digital Transformation with ICT Innovation. International Conference on ICT Convergence, vol. 2022-October, IEEE Computer Society, pp. 831-833, 13th International Conference on Information and Communication Technology Convergence, ICTC 2022, Jeju Island, Korea, Republic of, 19/10/22. https://doi.org/10.1109/ICTC55196.2022.9952406

Federated Deep Learning for RIS-assisted UAV-enabled Wireless Communications. / Park, Heejae; Nguyen, Tri Hai; Park, Laihyuk.
ICTC 2022 - 13th International Conference on Information and Communication Technology Convergence: Accelerating Digital Transformation with ICT Innovation. IEEE Computer Society, 2022. p. 831-833 (International Conference on ICT Convergence; Vol. 2022-October).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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T1 - Federated Deep Learning for RIS-assisted UAV-enabled Wireless Communications

AU - Park, Heejae

AU - Nguyen, Tri Hai

AU - Park, Laihyuk

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N2 - Unmanned aerial vehicles (UAVs) are becoming more crucial to enhancing spectral efficiency in the 6G network. However, there are problems such as blockage and energy-hungry. To solve these problems, an UAV-assisted reconfigurable intelligent surface (RIS) communication has been proposed for wireless system. RIS is a two-dimensional meta-surface, that is composed of cost-effective and energy-efficient passive reflecting elements, which can enhance spectral and energy efficiency. This paper formulates the problem of maximizing the achievable data rate by using long short-term memory and federated learning. We proposed the method that consists of two stages: 1) training stage for a global model in UAV, and 2) resource allocation stage in RIS controller.

AB - Unmanned aerial vehicles (UAVs) are becoming more crucial to enhancing spectral efficiency in the 6G network. However, there are problems such as blockage and energy-hungry. To solve these problems, an UAV-assisted reconfigurable intelligent surface (RIS) communication has been proposed for wireless system. RIS is a two-dimensional meta-surface, that is composed of cost-effective and energy-efficient passive reflecting elements, which can enhance spectral and energy efficiency. This paper formulates the problem of maximizing the achievable data rate by using long short-term memory and federated learning. We proposed the method that consists of two stages: 1) training stage for a global model in UAV, and 2) resource allocation stage in RIS controller.

KW - optimization

KW - re-source allocation

KW - RIS

KW - UAV communication

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BT - ICTC 2022 - 13th International Conference on Information and Communication Technology Convergence

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Park H, Nguyen TH, Park L. Federated Deep Learning for RIS-assisted UAV-enabled Wireless Communications. In ICTC 2022 - 13th International Conference on Information and Communication Technology Convergence: Accelerating Digital Transformation with ICT Innovation. IEEE Computer Society. 2022. p. 831-833. (International Conference on ICT Convergence). doi: 10.1109/ICTC55196.2022.9952406

Federated Deep Learning for RIS-assisted UAV-enabled Wireless Communications

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Keywords

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