Cooperative UAV Trajectory Design for Disaster Area Emergency Communications: A Multiagent PPO Method

Guan, Y; Zou, S; Peng, H; Ni, W; Sun, Y; Gao, H

Cooperative UAV Trajectory Design for Disaster Area Emergency Communications: A Multiagent PPO Method

Guan, Y Zou, S Peng, H Ni, W

Sun, Y Gao, H

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Publisher:: Institute of Electrical and Electronics Engineers (IEEE)
Publication Type:: Journal Article
Citation:: IEEE Internet of Things Journal, 2024, 11, (5), pp. 8848-8859
Issue Date:: 2024-03-01

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Field	Value	Language
dc.contributor.author	Guan, Y
dc.contributor.author	Zou, S
dc.contributor.author	Peng, H
dc.contributor.author	Ni, W https://orcid.org/0000-0002-4933-594X
dc.contributor.author	Sun, Y
dc.contributor.author	Gao, H
dc.date.accessioned	2024-08-07T04:24:52Z
dc.date.available	2024-08-07T04:24:52Z
dc.date.issued	2024-03-01
dc.identifier.citation	IEEE Internet of Things Journal, 2024, 11, (5), pp. 8848-8859
dc.identifier.issn	2372-2541
dc.identifier.issn	2327-4662
dc.identifier.uri	http://hdl.handle.net/10453/180184
dc.description.abstract	This article investigates the issue of cooperative real-time trajectory design for multiple unmanned aerial vehicles (UAVs) to support emergency communication in disaster areas. To restore communication links rapidly between mobile users (MUs) and the ground base stations, UAVs equipped with both radio frequency (RF) modules and free space optics (FSO) modules are utilized as relay nodes. Given the challenges of setting up a central controller for the UAVs and the urgency of emergency communication, the trajectory design problem for these UAVs is formulated as a distributed cooperative optimization problem. Based on the enhanced K-mean algorithm and multiagent PPO (MAPPO) algorithm, a cooperative trajectory design method, abbreviated as KMAPPO, is proposed for the UAVs to minimize interaction overhead and optimize deployment efficiency. Compared to the state-of-the-art deep reinforcement learning (DRL) methods, simulations reveal KMAPPO's superior performance. It converges 32% faster, boosts RF allocation efficiency, and augments FSO communication backhaul capacity.
dc.language	en
dc.publisher	Institute of Electrical and Electronics Engineers (IEEE)
dc.relation.ispartof	IEEE Internet of Things Journal
dc.relation.isbasedon	10.1109/JIOT.2023.3320796
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0805 Distributed Computing, 1005 Communications Technologies
dc.subject.classification	40 Engineering
dc.subject.classification	46 Information and computing sciences
dc.title	Cooperative UAV Trajectory Design for Disaster Area Emergency Communications: A Multiagent PPO Method
dc.type	Journal Article
utslib.citation.volume	11
utslib.for	0805 Distributed Computing
utslib.for	1005 Communications Technologies
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
utslib.copyright.status	closed_access	*
dc.date.updated	2024-08-07T04:24:46Z
pubs.issue	5
pubs.publication-status	Published
pubs.volume	11
utslib.citation.issue	5

Abstract:

This article investigates the issue of cooperative real-time trajectory design for multiple unmanned aerial vehicles (UAVs) to support emergency communication in disaster areas. To restore communication links rapidly between mobile users (MUs) and the ground base stations, UAVs equipped with both radio frequency (RF) modules and free space optics (FSO) modules are utilized as relay nodes. Given the challenges of setting up a central controller for the UAVs and the urgency of emergency communication, the trajectory design problem for these UAVs is formulated as a distributed cooperative optimization problem. Based on the enhanced K-mean algorithm and multiagent PPO (MAPPO) algorithm, a cooperative trajectory design method, abbreviated as KMAPPO, is proposed for the UAVs to minimize interaction overhead and optimize deployment efficiency. Compared to the state-of-the-art deep reinforcement learning (DRL) methods, simulations reveal KMAPPO's superior performance. It converges 32% faster, boosts RF allocation efficiency, and augments FSO communication backhaul capacity.

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http://hdl.handle.net/10453/180184