面向低空智联网的无人机网络切片资源管理机制研究

陈鸿杰; 郭昱甫; 吴凡; 张科; 熊凯

doi:10.11959/j.issn.1000-0801.2025053

您当前的位置：

首页 >

文章列表页 >

面向低空智联网的无人机网络切片资源管理机制研究

专题：低空智联网与低空安全 | 更新时间：2025-04-24

- 面向低空智联网的无人机网络切片资源管理机制研究
- Research on UAV network slicing resource management for low-altitude intelligent network
- 电信科学 2025年41卷第3期页码：38-51
- 作者机构：
  
  1.中国西南电子技术研究所，四川成都 610036
  2.电子科技大学信息与通信工程学院，四川成都 611731
- 作者简介：
  
  [ "陈鸿杰（1982- ），男，现就职于中国西南电子技术研究所，主要研究方向为通信与数据链。" ]
  [ "郭昱甫（2002- ），男，电子科技大学信息与通信工程学院硕士生，主要研究方向为无线网络资源管理与优化、智能无线网络技术。" ]
  [ "吴凡（1978- ），男，博士，电子科技大学信息与通信工程学院副教授，主要研究方向为边缘智能网络、无线网络资源管理与优化、智能无线网络技术。" ]
  [ "张科（1978- ），男，博士，电子科技大学信息与通信工程学院副教授，主要研究方向为移动边缘计算调度、下一代无线网络设计与优化、智能电网、物联网等。" ]
  [ "熊凯（1991- ），男，博士，电子科技大学信息与通信工程学院副研究员、在站博士后，主要研究方向为车联网资源分配、移动边缘计算和机器学习。" ]
- 基金信息：
  
  国家自然科学基金青年项目(62201122)
- DOI：10.11959/j.issn.1000-0801.2025053
  中图分类号： TN915.5
- 收稿日期：2025-01-24，
  
  修回日期：2025-03-05，
  
  纸质出版日期：2025-03-20
- 稿件说明：
移动端阅览
陈鸿杰,郭昱甫,吴凡等.面向低空智联网的无人机网络切片资源管理机制研究[J].电信科学,2025,41(03):38-51.

CHEN Hongjie,GUO Yufu,WU Fan,et al.Research on UAV network slicing resource management for low-altitude intelligent network[J].Telecommunications Science,2025,41(03):38-51.
陈鸿杰,郭昱甫,吴凡等.面向低空智联网的无人机网络切片资源管理机制研究[J].电信科学,2025,41(03):38-51. DOI： 10.11959/j.issn.1000-0801.2025053.

CHEN Hongjie,GUO Yufu,WU Fan,et al.Research on UAV network slicing resource management for low-altitude intelligent network[J].Telecommunications Science,2025,41(03):38-51. DOI： 10.11959/j.issn.1000-0801.2025053.

摘要

在低空智联网中，无人机作为空中通信基站、数据传输中继节点和移动网络终端的重要组成部分，凭借其卓越的机动性和适应性，广泛应用于扩展网络覆盖和支持多种业务服务。然而，由于低空智联网面临着网络拓扑动态变化、空域资源稀缺以及多样化业务需求等挑战，实现有限资源的高效编排和管理仍然是一项艰巨任务。为解决这一问题，通过对无人机网络进行端到端切片，构建满足特定需求的逻辑无人机网络架构。首先，设计了一种分群轨迹预测模型，用于确定分群接入节点的位置，为网络切片的资源预留与优化提供支持。基于此，提出了一种双时间尺度的资源管理框架：在大时间尺度上，采用非线性规划方法将切片重配置问题转化为约束优化问题，优化整体切片效益并合理预留资源；在小时间尺度上，通过针对切片内业务需求的资源调度策略，满足具体业务的传输服务质量（quality of service，QoS）需求。仿真结果表明，该方法增强了低空无人机智联网络在动态环境中的适应性与服务质量，为低空智联网复杂场景下的资源管理和业务保障提供了有效支持。

Abstract

In low-altitude intelligent networks

unmanned aerial vehicles (UAV) play a crucial role as aerial communication base stations

data relay nodes

and mobile network terminals. Leveraging their exceptional mobility and adaptability

UAV can extend network coverage and support a wide range of service applications. However

the challenges of dynamic network topology

constrained airspace resources

and diverse service demand pose significant difficulties in achieving efficient resource orchestration and management. To address these challenges

an end-to-end slicing approach for UAV network was introduced

enabling the construction of logical network architectures tailored to specific requirements. A cluster trajectory prediction model was developed to identify the positions of clustered access nodes

providing essential support for resource reservation and optimization in network slicing. Building on this

a dual time-scale resource management framework was proposed. At a larger time scale

the slice reconfiguration problem was transformed into a constrained optimization task by a nonlinear programming approach

maximizing overall slice efficiency and ensuring rational resource reservation. At a finer time scale

intra-slice resource scheduling strategies were implemented to meet the QoS requirements of specific services. Simulation results demonstrate that the proposed method significantly improves the communication performance of low-altitude dynamic intelligent networks. It enhances the adaptability and service quality of UAV network slicing in dynamic environments

offering effective support for resource management and service assurance in complex scenarios.

关键词

Keywords

references

BORODIN V , KOLESNICHENKO V , KOVALKINA N . Assessment of indicators for updating adjacency matrix of self-organizing flying ad hoc network [J ] . Journal of Aerospace Technology and Management , 2024 , 16 ( 1 ): 1 - 15 .

YANG J , SUN K X , HE H S , et al . Dynamic virtual topology aided networking and routing for aeronautical ad-hoc networks [J ] . IEEE Transactions on Communications , 2022 , 70 ( 7 ): 4702 - 4716 .

ZHAO J Y , FANG Z , GE J T , et al . A survey on identification of critical nodes in ad hoc network [C ] // Proceedings of the 2023 IEEE 6th Information Technology, Networking, Electronic and Automation Control Conference (ITNEC) . Piscataway : IEEE Press , 2023 : 1770 - 1774 .

MANDAR D , EITAN A , HÉLÈNE L C . Strategic resource pricing and allocation in a 5G network slicing Stackelberg game [J ] . IEEE Transactions on Network and Service Management , 2023 , 20 ( 1 ): 502 - 520 .

YUAN Z H , MUNTEAN G M . AirSlice: a network slicing framework for UAV communications [J ] . IEEE Communications Magazine , 2020 , 58 ( 11 ): 62 - 68 .

吴思雷 . 基于深度强化学习的多无人机轨迹联合调控方法研究 [D ] . 北京 : 北京邮电大学 , 2022 .

WU S L . Research on joint trajectory control method of multi-UAV based on deep reinforcement learning [D ] . Beijing : Beijing University of Posts and Telecommunications , 2022 .

罗娟 , 徐岳阳 , 李仁发 . 网络虚拟化中动态资源分配算法研究 [J ] . 通信学报 , 2011 , 32 ( 7 ): 64 - 70 .

LUO J , XU Y Y , LI R F . Dynamical resource allocation algorithm research in network virtualization [J ] . Journal on Communications , 2011 , 32 ( 7 ): 64 - 70 .

DHANIK S , VIJAI C , REDDY Y M , et al . Ad hoc networks: adaptable connectivity for contemporary business needs [C ] // Proceedings of the 2023 10th IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON) . Piscataway : IEEE Press , 2023 : 1694 - 1698 .

TANG J H , SHIM B , QUEK T Q S . Service multiplexing and revenue maximization in sliced C-RAN incorporated with URLLC and multicast eMBB [J ] . IEEE Journal on Selected Areas in Communications , 2019 , 37 ( 4 ): 881 - 895 .

WANG G , FENG G , QUEK T Q S , et al . Reconfiguration in network slicing: optimizing the profit and performance [J ] . IEEE Transactions on Network and Service Management , 2019 , 16 ( 2 ): 591 - 605 .

ZHANG H , WONG V W S . A two-timescale approach for network slicing in C-RAN [J ] . IEEE Transactions on Vehicular Technology , 2020 , 69 ( 6 ): 6656 - 6669 .

CHEN X J , NI W , CHEN T Y , et al . Multi-timescale online optimization of network function virtualization for service chaining [J ] . IEEE Transactions on Mobile Computing , 2019 , 18 ( 12 ): 2899 - 2912 .

CAMARGO J S , CORONADO E , RAMIREZ W , et al . Dynamic slicing reconfiguration for virtualized 5G networks using ML forecasting of computing capacity [J ] . Computer Networks , 2023 ( 236 ): 110001 .

GUAN W Q , ZHANG H J , LEUNG V C M . Customized slicing for 6G: enforcing artificial intelligence on resource management [J ] . IEEE Network , 2021 , 35 ( 5 ): 264 - 271 .

TOKUDA K , SATO T , OKI E . Network slice reconfiguration with deep reinforcement learning under variable number of service function chains [J ] . Computer Networks , 2023 ( 224 ): 109636 .

HUA Y X , LI R P , ZHAO Z F , et al . GAN-powered deep distributional reinforcement learning for resource management in network slicing [J ] . IEEE Journal on Selected Areas in Communications , 2020 , 38 ( 2 ): 334 - 349 .

HUANG R L , GUO M , GU C J , et al . Toward scalable and efficient hierarchical deep reinforcement learning for 5G RAN slicing [J ] . IEEE Transactions on Green Communications and Networking , 2023 , 7 ( 4 ): 2153 - 2162 .

TU H Y , BELLAVISTA P , ZHAO L Q , et al . Priority-based load balancing with multiagent deep reinforcement learning for space-air-ground integrated network slicing [J ] . IEEE Internet of Things Journal , 2024 , 11 ( 19 ): 30690 - 30703 .

ZHANG S L . An overview of network slicing for 5G [J ] . IEEE Wireless Communications , 2019 , 26 ( 3 ): 111 - 117 .

BASAR E , YILDIRIM I . Reconfigurable intelligent surfaces for future wireless networks: a channel modeling perspective [J ] . IEEE Wireless Communications , 2021 , 28 ( 3 ): 108 - 114 .

浏览量

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

基于OS-MBRL的网络切片资源动态分配方法研究

面向工业时敏业务的5G TSN融合网络切片资源调度

IPv6+智能骨干网络技术研究

使能未来工厂的5G能力综述

面向智能制造的5G网络部署方案研究