Minh Le Nguyen, Tinh T. Bui, L. Nguyen, E. Garcia-Palacios, H. Zepernick, T. Duong
{"title":"Real-Time Large-Scale 6G Satellite-UAV Networks","authors":"Minh Le Nguyen, Tinh T. Bui, L. Nguyen, E. Garcia-Palacios, H. Zepernick, T. Duong","doi":"10.1109/SSP53291.2023.10208078","DOIUrl":null,"url":null,"abstract":"In this paper, we consider an Internet-of-Things network supported by several satellites and multiple cache-assisted unmanned aerial vehicles (UAVs). We propose an optimisation problem with the aim of minimising the total network latency. To reduce the complexity of the original problem, it is divided into three sub-problems, namely, clustering ground users associated with UAVs, cache placement in UAVs (to support the network in avoiding backhaul congestion), and power allocation for satellites and UAVs. A non-cooperative game is designed to obtain the solution to the clustering problem; a genetic algorithm, which is powerful in the scenario of many variables, is employed to obtain the optimal solution to the high-complexity caching problem; and a quick estimation technique is used for power allocation. The total network latency is then minimised by using alternating optimisation technique. Numerical results prove the efficiency of our methods compared to other traditional ones.","PeriodicalId":296346,"journal":{"name":"2023 IEEE Statistical Signal Processing Workshop (SSP)","volume":"144 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2023 IEEE Statistical Signal Processing Workshop (SSP)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SSP53291.2023.10208078","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
In this paper, we consider an Internet-of-Things network supported by several satellites and multiple cache-assisted unmanned aerial vehicles (UAVs). We propose an optimisation problem with the aim of minimising the total network latency. To reduce the complexity of the original problem, it is divided into three sub-problems, namely, clustering ground users associated with UAVs, cache placement in UAVs (to support the network in avoiding backhaul congestion), and power allocation for satellites and UAVs. A non-cooperative game is designed to obtain the solution to the clustering problem; a genetic algorithm, which is powerful in the scenario of many variables, is employed to obtain the optimal solution to the high-complexity caching problem; and a quick estimation technique is used for power allocation. The total network latency is then minimised by using alternating optimisation technique. Numerical results prove the efficiency of our methods compared to other traditional ones.