多网融合无人机网络辅助的低空盲区接入系统- “空天地一体化智能网联”专刊

  • 梁一博 ,
  • 朱小军 ,
  • 董超
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  • 1. 南京航空航天大学计算机科学与技术学院
    2. 南京航空航天大学

收稿日期: 2025-10-13

  修回日期: 2026-01-06

  网络出版日期: 2026-01-09

基金资助

国家自然科学基金

Multi-network integrated UAV network assisted low-altitude access system for blind zone

  • LIANG Yi-Bo ,
  • ZHU Xiao-Jun ,
  • DONG Chao
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  • 1. College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics
    2.

Received date: 2025-10-13

  Revised date: 2026-01-06

  Online published: 2026-01-09

Supported by

National Natural Science Foundation of China

摘要

灾害现场、海上平台、江面上空等区域存在蜂窝信号盲区,低空飞行器工作时面临无法接入系统、难以与后端控制中心通信的问题。现有方法通过在地面上部署专用蜂窝基站,或在无人机上加装卫星通信模块,实现网络接入,成本较高。设计并实现了多网融合无人机网络辅助的接入系统,该系统由网关无人机、中继无人机、提供接入服务的无人机组成,无人机之间通过自组网连接,将数据从提供接入服务的无人机,通过中继无人机多跳转发至网关无人机。系统使用商用的低成本嵌入式设备与自组网模块,实现了自动网络配置、无缝漫游、会话保持等功能。在原型系统实验中,无人机节点能在3秒内完成自组织部署,在0.65秒内实现漫游切换,实现了上行吞吐量11.15 Mbps,下行吞吐量15.98 Mbps,在低空动态变化的网络拓扑场景中,系统能够在1.5秒内完成路由收敛,低于OLSR路由协议的4.15秒,有效通信距离延伸至近1 km,单节点综合能耗不超过100W。系统在覆盖范围、连接稳定性和业务连续性方面表现良好,以低成本为低空通信盲区提供可靠的网络接入服务。

本文引用格式

梁一博 , 朱小军 , 董超 . 多网融合无人机网络辅助的低空盲区接入系统- “空天地一体化智能网联”专刊[J]. 航空学报, 0 : 1 -0 . DOI: 10.7527/S1000-6893.2026.32901

Abstract

In areas such as disaster sites, offshore platforms, and above river surfaces, there are signal blind spots for cellular networks. When low-altitude aircraft operate, they may not be able to connect to the system and communicate with the control center. Existing methods achieve network coverage by deploying dedicated cellular base stations on the ground or installing satellite communication modules on the aircraft, but these methods are expensive. A multi-network fusion unmanned aerial vehicle (UAV) network-assisted access system has been designed and implemented. This system consists of gateway UAVs, relay UAVs, and UAVs providing access services. The UAVs are connected through ad hoc networks, and data is relayed from the UAV providing access services to the gateway UAV via the relay UAVs and multi-hop transmission. The system uses commercial low-cost embedded devices and ad hoc network modules to achieve functions such as automatic network configuration, seamless roaming, and session persistence. In prototype system experiments, UAV nodes can complete self-organization deployment within 3 seconds and achieve roaming switching within 0.65 seconds, with an uplink throughput of 11.15 Mbps and a downlink throughput of 15.98 Mbps. Under dynamically changing low-altitude topologies, the system completes routing convergence within 1.5 seconds, significantly outperforming the OLSR routing protocol about 4.15 seconds. The effective communication distance is extended to nearly 1 km, and the per-node total power consumption remains below 100 W, which is considerably lower than existing UAV-based communication solutions. The system performs well in terms of coverage, connection stability, and business continuity, providing reliable network access services for low-altitude communication blind areas at a low cost.

参考文献

[1]樊邦奎,李云,张瑞雨.浅析低空智联网与无人机产业应用[J].地理科学进展, 2021, 40(9):1441-1450
[2]董超,沈赟,屈毓锛.基于无人机的边缘智能计算研究综述[J].智能科学与技术学报, 2020, 2(3):227-239
[3]樊邦奎,吴启晖.低空智联网”专栏序言[J].数据采集与处理, 2024, 39(1):1-1
[4]董超, 经宇骞, 屈毓锛, 等.面向低空智联网频谱认知与决策的云边端融合体系架构[J].通信学报, 2023, 44(11):1-12
[5]刘玮, 侯杰, 黄志勇, 等.基于5G空地一体组网技术的机动通信保障方案与性能研究[J].电信科学, 2024(S2): 1-9.[J].电信科学, 2024, 增刊(2):1-9
[6]刘言, 赵辰乾, 闫瑞东, 等.基于云网协同的通感算智赋能卫星网络技术[J].无线电通信技术, 2025, 51(4):733-741
[7]WANG A, ZHU X, QU Y, et al.Quick gateway switching for reliable data collection in multi-gateway UAV networks with gateway failures[J].IEEE Transactions on Vehicular Technology, 2025, :-
[8]武燕燕, 吴松.空天地海一体化网络边缘计算的资源管理研究[J].移动通信, 2024, 48(9):124-131
[9]刘勇军, 巫江.4G&5G融合核心网的高效能数智测试方法探讨[J].通信与信息技术, 2025, (3):31-35
[10]杨波.空天地海一体化网络的通感一体技术应用及展望[J]. 通信世界, 2025(14): 40-43.[J].通信世界, 2025, (14):40-43
[11]MESSAOUDI K, BAZ A, OUBBATI O S, et al.UGV charging stations for UAV-assisted AoI-aware data collection[J].IEEE Transactions on Cognitive Communications and Networking, 2024, 10(6):2325-2343
[12]AMEUR A I, OUBBATI O S, LAKAS A, et al.Efficient vehicular data sharing using aerial P2P backbone,[J].IEEE Transactions on Intelligent Vehicles, 2024, :1-14
[13]DUTRIEZ C, OUBBATI O S, GUEGUEN C, et al.Energy efficiency relaying election mechanism for 5G internet of things: A deep reinforcement learning technique[C]// IEEE Wireless Communications and Networking Conference (WCNC). Piscataway: IEEE, 2024.
[14]POJDA J, WOLFF A, SBEITI M, et al.Performance analysis of mesh routing protocols for UAV swarming applications[C]// 8th International Symposium on Wireless Communication Systems.Piscataway: IEEE, 2011.
[15]ZHENG Z, YONG T, LI J, et al.Simulation research of UANET based on batman-adv routing protocol[C]// IEEE International Conference on Unmanned Systems (ICUS). Piscataway: IEEE, 2022.
[16]KIRAN K, KAUSHIK N P, SHARATH S, et al.Experimental evaluation of batman and batman-adv routing protocols in a mobile testbed[C]// TENCON - IEEE Region 10 Conference. Piscataway: IEEE, 2018.
[17]GARROPPO R G, GIORDANO S, TAVANTI L.Experimental evaluation of two open source solutions for wireless mesh routing at layer two[C]// IEEE 5th International Symposium on Wireless Pervasive Computing. Piscataway: IEEE, 2010.
[18]SU Y, NGUYEN, SEKIYA H.Recovery time evaluation of ad-hoc routing protocols in IoT-blockchain[C]// IEEE 4th Global Conference on Life Sciences and Technologies (LifeTech). Piscataway: IEEE, 2022.
[19]KARMAKAR P, SHAH V K, ROY S, et al.Reliable backhauling in aerial communication networks against UAV failures: A deep reinforcement learning approach[J].IEEE Transactions on Network and Service Management, 2022, 19(3):2798-2811
[20]ZHANG J, WANG T, WANG J, et al.Multi-UAV collaborative surveillance network recovery via deep reinforcement learning[J].IEEE Internet of Things Journal, 2024, 11(21):34528-34540
[21]CROWE W B, OH T T.Continual 4G cell coverage for the nodes during a gateway failure using batman-adv[C]// International Conference on Information and Communication Technology Convergence (ICTC). Piscataway: IEEE, 2020.
[22]王伟, 陈志刚, 何春蛟, 等.灾害应急通信场景下无人机基站位置部署与路径规划[J]. 交通运输工程与信息学报, 2025: 1-19.
[23]ROLLY R M, MALARVEZHI P, LAGKAS T D.Unmanned aerial vehicles: Applications,techniques,and challenges as aerial base stations[J].International Journal of Distributed Sensor Networks, 2022, 18(9):15501329221123933-
[24]SIVALINGAM T, MANOSHA K B S, RAJATHEVA N, et al.Positioning of multiple unmanned aerial vehicle base stations in future wireless network[C]// 2020 IEEE 91st Vehicular Technology Conference (VTC2020-Spring). Piscataway: IEEE, 2020: 1-6.
[25]ZHAO Z, CUMINO P, ESPOSITO C, et al.Smart unmanned aerial vehicles as base stations placement to improve the mobile network operations[J]. Computer Communications, 2022, 181: 45-57.
[26]XU C, LIAO X, TAN J, et al.Recent research progress of unmanned aerial vehicle regulation policies and technologies in urban low altitude[J]. IEEE Access, 2020, 8: 74175-74194.
[27]LI Z, LI S, LU J, et al.Air route network planning method of urban low-altitude logistics UAV with double-layer structure[J].Drones, 2025, 9(3):193-
[28]HE D, YUAN W, WU J, et al.Ubiquitous UAV communication enabled low-altitude economy: Applications, techniques, and 3GPP' s efforts[J]. IEEE Network, 2025.
[29]AHMED F, JENIHHIN M.A survey on UAV computing platforms: A hardware reliability perspective[J].Sensors, 2022, 22(16):6286-
[30]KHAN M A, KHAN I U, SAFI A, et al.Dynamic routing in flying ad-hoc networks using topology-based routing protocols[J].Drones, 2018, 2(3):27-
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