{"title":"An improved affinity propagation method for maximising system sum rate and minimising interference for 3D multi-UAV placement in disaster area","authors":"Nooshin Boroumand Jazi, Farhad Faghani, Mahmoud Daneshvar Farzanegan","doi":"10.1049/ntw2.12143","DOIUrl":null,"url":null,"abstract":"<p>In emergencies where several ground base stations (GBS) are no longer available, mobile base stations based on unmanned aerial vehicles (UAVs) can efficiently resolve coverage issues in remote areas due to their cost-effectiveness and versatility. Natural disasters, such as a deluge, cause damage to the terrestrial wireless infrastructure. The main challenge in these systems is to determine the optimal 3D placement of UAVs to meet the dynamic demand of users and minimise interference. Various mathematical frameworks and efficient algorithms are suggested for designing, optimising, and deploying UAV-based communication systems. This paper investigates the challenges of 3D UAV placement through machine learning (ML) and enhanced affinity propagation (EAP). Lastly, the simulation results indicate that the proposed approach improves the system sum rate, interference, and coverage performance compared to DBSCAN, k-means, and k-means++ methods. Therefore, this paper identifies UAVs' most effective 3D placement, including minimising the number of UAVs, maximising the number of covered users, and maximising the system sum rate for an arbitrary distribution of users in the disaster area. Additionally, this paper addresses the issue of interference minimisation.</p>","PeriodicalId":46240,"journal":{"name":"IET Networks","volume":"14 1","pages":""},"PeriodicalIF":1.3000,"publicationDate":"2025-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/ntw2.12143","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IET Networks","FirstCategoryId":"1085","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1049/ntw2.12143","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
引用次数: 0
Abstract
In emergencies where several ground base stations (GBS) are no longer available, mobile base stations based on unmanned aerial vehicles (UAVs) can efficiently resolve coverage issues in remote areas due to their cost-effectiveness and versatility. Natural disasters, such as a deluge, cause damage to the terrestrial wireless infrastructure. The main challenge in these systems is to determine the optimal 3D placement of UAVs to meet the dynamic demand of users and minimise interference. Various mathematical frameworks and efficient algorithms are suggested for designing, optimising, and deploying UAV-based communication systems. This paper investigates the challenges of 3D UAV placement through machine learning (ML) and enhanced affinity propagation (EAP). Lastly, the simulation results indicate that the proposed approach improves the system sum rate, interference, and coverage performance compared to DBSCAN, k-means, and k-means++ methods. Therefore, this paper identifies UAVs' most effective 3D placement, including minimising the number of UAVs, maximising the number of covered users, and maximising the system sum rate for an arbitrary distribution of users in the disaster area. Additionally, this paper addresses the issue of interference minimisation.
IET NetworksCOMPUTER SCIENCE, INFORMATION SYSTEMS-
CiteScore
5.00
自引率
0.00%
发文量
41
审稿时长
33 weeks
期刊介绍:
IET Networks covers the fundamental developments and advancing methodologies to achieve higher performance, optimized and dependable future networks. IET Networks is particularly interested in new ideas and superior solutions to the known and arising technological development bottlenecks at all levels of networking such as topologies, protocols, routing, relaying and resource-allocation for more efficient and more reliable provision of network services. Topics include, but are not limited to: Network Architecture, Design and Planning, Network Protocol, Software, Analysis, Simulation and Experiment, Network Technologies, Applications and Services, Network Security, Operation and Management.