• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利：CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法，用中文像聊天一样搜索，搜遍4000万医学文献。AI智能推荐，让科研检索更轻松。

立即免费搜索

文件翻译

保留排版，准确专业，支持PDF/Word/PPT等文件格式，支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述，25分钟生成高质量综述，智能提取关键信息，辅助科研写作。

立即免费体验

基于联合用户关联和三维无人机部署的高空气球-无人机协作方案吞吐量最大化研究。

Investigation of a HAP-UAV Collaboration Scheme for Throughput Maximization via Joint User Association and 3D UAV Placement.

机构信息

Department of Electrical, Computer and Biomedical Engineering, Toronto Metropolitan University, Toronto, ON M5B 2K3, Canada.

Department of Computer Science and Engineering, American University of Ras Al Khaimah, Ras al Khaimah P.O. Box 10021, United Arab Emirates.

出版信息

Sensors (Basel). 2023 Jul 2;23(13):6095. doi: 10.3390/s23136095.

DOI:10.3390/s23136095
PMID:37447944
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10346792/
Abstract

In this paper, a collaboration scheme between a high-altitude platform (HAP) and several unmanned aerial vehicles (UAVs) for wireless communication networks is investigated. The main objective of this study is to maximize the total downlink throughput of the ground users by optimizing the UAVs' three-dimensional (3D) placements and user associations. An optimization problem is formulated and a separate genetic-algorithm-based approach is proposed to solve the problem. The K-means algorithm is also utilized to find the initial UAV placement to reduce the convergence time of the proposed genetic-algorithm-based allocation. The performance of the proposed algorithm is analyzed in terms of convergence time, complexity, and fairness. Finally, the simulation results show that the proposed HAP-UAV integrated network achieves a higher total throughput through joint user association and UAV placement schemes compared to a scheme with a single HAP serving all users.

摘要

在本文中，研究了高空平台 (HAP) 和多个无人机 (UAV) 之间用于无线通信网络的协作方案。本研究的主要目的是通过优化 UAV 的三维 (3D) 位置和用户关联来最大化地面用户的总下行链路吞吐量。本文提出了一个优化问题，并提出了一种基于遗传算法的单独方法来解决该问题。K-means 算法也被用来找到初始的 UAV 位置，以减少基于遗传算法的分配的收敛时间。所提出算法的性能在收敛时间、复杂度和公平性方面进行了分析。最后，仿真结果表明，与单个 HAP 为所有用户服务的方案相比，所提出的 HAP-UAV 集成网络通过联合用户关联和 UAV 放置方案实现了更高的总吞吐量。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f556/10346792/4ab521fb09f2/sensors-23-06095-g016.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f556/10346792/1a710fe17390/sensors-23-06095-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f556/10346792/b0f2a23c833e/sensors-23-06095-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f556/10346792/1cb40cec284a/sensors-23-06095-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f556/10346792/de4cf3b3feab/sensors-23-06095-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f556/10346792/624242675b67/sensors-23-06095-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f556/10346792/58bef713fe3b/sensors-23-06095-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f556/10346792/fd6546f06709/sensors-23-06095-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f556/10346792/f4854653220a/sensors-23-06095-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f556/10346792/c40593b6443c/sensors-23-06095-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f556/10346792/a488f71a638a/sensors-23-06095-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f556/10346792/8de02a5a9530/sensors-23-06095-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f556/10346792/01ff3a5e61ff/sensors-23-06095-g012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f556/10346792/7e0eca28caf6/sensors-23-06095-g013.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f556/10346792/f1453e9667f2/sensors-23-06095-g014.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f556/10346792/bee0724c1a81/sensors-23-06095-g015.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f556/10346792/4ab521fb09f2/sensors-23-06095-g016.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f556/10346792/1a710fe17390/sensors-23-06095-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f556/10346792/b0f2a23c833e/sensors-23-06095-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f556/10346792/1cb40cec284a/sensors-23-06095-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f556/10346792/de4cf3b3feab/sensors-23-06095-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f556/10346792/624242675b67/sensors-23-06095-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f556/10346792/58bef713fe3b/sensors-23-06095-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f556/10346792/fd6546f06709/sensors-23-06095-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f556/10346792/f4854653220a/sensors-23-06095-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f556/10346792/c40593b6443c/sensors-23-06095-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f556/10346792/a488f71a638a/sensors-23-06095-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f556/10346792/8de02a5a9530/sensors-23-06095-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f556/10346792/01ff3a5e61ff/sensors-23-06095-g012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f556/10346792/7e0eca28caf6/sensors-23-06095-g013.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f556/10346792/f1453e9667f2/sensors-23-06095-g014.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f556/10346792/bee0724c1a81/sensors-23-06095-g015.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f556/10346792/4ab521fb09f2/sensors-23-06095-g016.jpg

相似文献

1
Investigation of a HAP-UAV Collaboration Scheme for Throughput Maximization via Joint User Association and 3D UAV Placement.基于联合用户关联和三维无人机部署的高空气球-无人机协作方案吞吐量最大化研究。
Sensors (Basel). 2023 Jul 2;23(13):6095. doi: 10.3390/s23136095.
2
Power-Efficient Wireless Coverage Using Minimum Number of UAVs.使用最少数量的无人机实现高能效的无线覆盖。
Sensors (Basel). 2021 Dec 29;22(1):223. doi: 10.3390/s22010223.
3
Energy-efficient UAV communication: A NOMA scheme with resource allocation and trajectory optimization.高能效无人机通信：一种具有资源分配和轨迹优化的非正交多址方案。
PLoS One. 2024 Apr 16;19(4):e0301819. doi: 10.1371/journal.pone.0301819. eCollection 2024.
4
Minimum-Throughput Maximization for Multi-UAV-Enabled Wireless-Powered Communication Networks.多无人机助力的无线供能通信网络中的最小吞吐量最大化。
Sensors (Basel). 2019 Mar 27;19(7):1491. doi: 10.3390/s19071491.
5
Power Allocation and Energy Cooperation for UAV-Enabled MmWave Networks: A Multi-Agent Deep Reinforcement Learning Approach.无人机增强毫米波网络的功率分配和能量合作：一种多智能体深度强化学习方法。
Sensors (Basel). 2021 Dec 30;22(1):270. doi: 10.3390/s22010270.
6
Biological Intelligence Inspired Trajectory Design for Energy Harvesting UAV Networks.受生物智能启发的能量收集无人机网络轨迹设计。
Sensors (Basel). 2023 Jan 11;23(2):863. doi: 10.3390/s23020863.
7
Joint UAVs' Load Balancing and UEs' Data Rate Fairness Optimization by Diffusion UAV Deployment Algorithm in Multi-UAV Networks.多无人机网络中基于扩散无人机部署算法的联合无人机负载均衡与用户设备数据速率公平性优化
Entropy (Basel). 2021 Nov 7;23(11):1470. doi: 10.3390/e23111470.
8
Joint Resource Management and Trajectory Optimization for UAV-Enabled Maritime Network.无人机支持的海上网络的联合资源管理和轨迹优化。
Sensors (Basel). 2022 Dec 13;22(24):9763. doi: 10.3390/s22249763.
9
Clustering and Beamwidth Optimization for UAV-Assisted Wireless Communication.无人机辅助无线通信的聚类与波束宽度优化
Sensors (Basel). 2023 Dec 4;23(23):9614. doi: 10.3390/s23239614.
10
Trajectory optimization of UAV-IRS assisted 6G THz network using deep reinforcement learning approach.基于深度强化学习方法的无人机-智能反射面辅助6G太赫兹网络轨迹优化
Sci Rep. 2024 Aug 9;14(1):18501. doi: 10.1038/s41598-024-68459-8.

本文引用的文献

1
Machine Learning-Based Methods for Enhancement of UAV-NOMA and D2D Cooperative Networks.基于机器学习的无人机非正交多址和 D2D 协作网络增强方法。
Sensors (Basel). 2023 Mar 10;23(6):3014. doi: 10.3390/s23063014.