An optimal model and solution of deployment of airships for high altitude platforms

Xuyu Wang, Xinbo Gao, R. Zong, Peng Cheng
{"title":"An optimal model and solution of deployment of airships for high altitude platforms","authors":"Xuyu Wang, Xinbo Gao, R. Zong, Peng Cheng","doi":"10.1109/WCSP.2010.5633821","DOIUrl":null,"url":null,"abstract":"In future communication system, the demand for high capacity is a challenging problem for wireless services, especially for delivery of the 'last mile'. A potential solution is offered by the high altitude platforms (HAPs), which can utilize the best character and tradeoff of both satellite and terrestrial networks. Since the performance of the HAPs depends on the structure of network, how to deploy the nodes of airships for HAPs is increasingly important. In this paper, an optimal model of deployment of airships for HAPs is constructed and solved based on genetic algorithm. First, a heterogeneous system including terrestrial layer, HAP layer and GEO layer is given. Then, an optimal model with objective function of maximum entropy and minimum delay is established to optimize the deployment of airships for HAPs. Finally, a modified genetic algorithm (GA) is employed to optimize the objective function and get the optimal solution to the model. Simulation results show that the established objective function and the solution based on GA can reach the goal of on-demand deployment of airship for HAPs.","PeriodicalId":448094,"journal":{"name":"2010 International Conference on Wireless Communications & Signal Processing (WCSP)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"9","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2010 International Conference on Wireless Communications & Signal Processing (WCSP)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/WCSP.2010.5633821","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 9

Abstract

In future communication system, the demand for high capacity is a challenging problem for wireless services, especially for delivery of the 'last mile'. A potential solution is offered by the high altitude platforms (HAPs), which can utilize the best character and tradeoff of both satellite and terrestrial networks. Since the performance of the HAPs depends on the structure of network, how to deploy the nodes of airships for HAPs is increasingly important. In this paper, an optimal model of deployment of airships for HAPs is constructed and solved based on genetic algorithm. First, a heterogeneous system including terrestrial layer, HAP layer and GEO layer is given. Then, an optimal model with objective function of maximum entropy and minimum delay is established to optimize the deployment of airships for HAPs. Finally, a modified genetic algorithm (GA) is employed to optimize the objective function and get the optimal solution to the model. Simulation results show that the established objective function and the solution based on GA can reach the goal of on-demand deployment of airship for HAPs.
高空平台飞艇部署优化模型及解决方案
在未来的通信系统中,对大容量的需求是无线业务,特别是“最后一英里”传输的一个具有挑战性的问题。高空平台(HAPs)提供了一个潜在的解决方案,它可以利用卫星和地面网络的最佳特性和权衡。由于HAPs的性能与网络结构密切相关,因此如何为HAPs部署飞艇节点变得越来越重要。本文建立了飞艇部署的最优模型,并基于遗传算法进行了求解。首先,给出了一个包含地面层、HAP层和GEO层的异构系统。在此基础上,建立了以最大熵最小时延为目标函数的飞艇部署优化模型。最后,采用改进的遗传算法对目标函数进行优化,得到模型的最优解。仿真结果表明,所建立的目标函数和基于遗传算法的求解方法能够达到飞艇按需部署的目标。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信