面向网络容量最大化的FANET韧性重构机制

doi:10.7527/S1000-6893.2026.33854

Abstract

Abstract:

This paper investigates the resilient reconfiguration problem of Flying Ad Hoc Networks （FANETs） after topology damage. When external disturbances cause partial node failures， the network is required to reconstruct connectivity over the remaining active nodes under limited resources and restore its information-carrying capability as much as possible. In this paper， the network capacity under multi-hop transmission is adopted as the primary metric for network performance， which is defined as the sum of end-to-end effective capacities over all node pairs with bottleneck links and hop-count penalty taken into account. Based on this metric， the network topology， node geometric positions， and link communication resources are jointly optimized to improve the reconfigured network capacity while controlling reconstruction time and energy consumption. First， Unmanned Aerial Vehicle （UAV） states and resource budgets are treated as node features， while link conditions are treated as edge features and embedded into a graph attention network to characterize the network topology. Second， a hierarchical optimization framework is proposed， where the upper layer adopts a centralized proximal policy optimization method to generate an adjacency matrix for topology reconstruction， while the lower layer employs a multi-agent proximal policy optimization method to perform distributed displacement adjustment under a given topology. Finally， by maximizing the minimum link capacity as a link-level fairness surrogate objective， node transmit power and bandwidth are allocated to improve network capacity while controlling reconstruction time and cost. Simulation results demonstrate that， compared with other schemes， the proposed method can significantly restore the network capacity after topology damage while maintaining relatively low reconstruction time and energy consumption， thereby exhibiting favorable resilient reconfiguration performance.

Key words: flying ad hoc network, resilient reconfiguration, graph attention network, proximal policy optimization, multi-agent systems

CLC Number:

V279
TN92

Guangyu LEI, Tianhao LIANG, Xinglin CHEN, Yuqi PING, Weijie YUAN, Tingting ZHANG. Resilient reconfiguration of FANETs for network capacity maximization[J]. Acta Aeronautica et Astronautica Sinica, 2026, 47(18): 533854.

Figures/Tables 10

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References 29

[1]	LIANG T H， ZHANG T T， WU Q Q， et al. Age of information based scheduling for UAV aided localization and communication［J］. IEEE Transactions on Wireless Communications， 2024， 23（5）： 4610-4626.
[2]	LIANG T H， ZHANG T T， ZHOU S， et al. UAV-aided localization and communication： Joint frame structure， beamwidth， and power allocation［J］. IEEE Journal of Selected Areas in Sensors， 2024， 1： 154-165.
[3]	LEI G Y， LIANG T H， PING Y Q， et al. Towards secure low-altitude airspace： MLLM-enabled UAV intent recognition［J］. IEEE Internet of Things Magazine， 2026， 9（2）： 93-100.
[4]	SMITH P， HUTCHISON D， STERBENZ J P G， et al. Network resilience： A systematic approach［J］. IEEE Communications Magazine， 2011， 49（7）： 88-97.
[5]	MAHMOOD N H， SAMARAKOON S， PORAMBAGE P， et al. Resilient-by-design： A resilience framework for future wireless networks［J］. IEEE Communications Magazine， 2025， 63（11）： 158-164.
[6]	PHADKE A， MEDRANO F A. Towards resilient UAV swarms： A breakdown of resiliency requirements in UAV swarms［J］. Drones， 2022， 6（11）： 340.
[7]	张陆游，刘子豪，胡铮，等. 基于具身智能的空基信息韧性网络发展研究［J］. 通信学报， 2025， 46（12）： 14-30.
	ZHANG L Y， LIU Z H， HU Z， et al. Research on the development of airborne information resilience networks based on embodied intelligence［J］. Journal on Communications， 2025， 46（12）： 14-30 （in Chinese）.
[8]	YUAN H， LI R Z， WANG L H， et al. Toward resilient communication architecture： Online network reconfiguration for UAV failure［J］. Computer Networks， 2025， 263： 111210.
[9]	WANG Y Y， WANG X， WEI C， et al. UAV swarm network topology self-healing via graph-based deep reinforcement learning［C］∥ 2025 IEEE Wireless Communications and Networking Conference （WCNC）. Piscataway： IEEE Press， 2025： 1-6.
[10]	ZEAR A， RANGA V. UAVs assisted network partition detection and connectivity restoration in wireless sensor and actor networks［J］. Ad Hoc Networks， 2022， 130： 102823.
[11]	WANG H B， HUIBIN WANG M C， MING CHEN X W. A k-hop constrained reachability based proactive connectivity maintaining mechanism of UAV swarm networks［J］. Journal of Internet Technology， 2023， 24（6）： 1329-1341.
[12]	SUN W H， WANG H， QIN Z. Connection in the air： QoE-centric multi-hop transmission in UAV-assisted emergency communication system［J］. Ad Hoc Networks， 2026， 180： 104051.
[13]	ALMANSOR M J， DIN N M， BAHARUDDIN M Z， et al. Routing protocols strategies for flying Ad-Hoc network （FANET）： Review， taxonomy， and open research issues［J］. Alexandria Engineering Journal， 2024， 109： 553-577.
[14]	MITTAL P， SHAH S， AGARWAL A， et al. Interference aware joint power control and routing optimization in multi-UAV FANETs［J］. Ad Hoc Networks， 2023， 150： 103280.
[15]	ZHANG T Y， ZHENG Y， SHENG M， et al. Efficient air-ground collaborative routing strategy for UAV-assisted MANETs［J］. IEEE Transactions on Vehicular Technology， 2026， 75（3）： 4722-4733.
[16]	GARG S， IHLER A， BENTLEY E S， et al. A hybrid reactive routing protocol for decentralized UAV networks［J］. IEEE Transactions on Aerospace and Electronic Systems， 2025， 61（5）： 11683-11701.
[17]	CHEN X H， SHENG M， ZHAO N， et al. Enhancing network capacity with transmission range optimization in UAV ad hoc networks［J］. IEEE Transactions on Communications， 2025， 73（7）： 4766-4779.
[18]	MITTAL P， SHAH S， AGARWAL A. Power-efficient joint link selection and multihop routing for throughput maximization in UAV assisted FANETs［C］∥ 2022 IEEE 33rd Annual International Symposium on Personal， Indoor and Mobile Radio Communications （PIMRC）. Piscataway： IEEE Press， 2022： 1282-1287.
[19]	HU T Z， ZONG Y， LU N Y， et al. Dynamic recovery and a resilience metric for UAV swarms under attack［J］. Drones， 2025， 9（8）： 589.
[20]	MALHOTRA A， KAUR S. A comprehensive review on recent advancements in routing protocols for flying ad hoc networks［J］. Transactions on Emerging Telecommunications Technologies， 2022， 33（3）： e3688.
[21]	HUANG Z Y， WU W W， FU C C， et al. Communication-topology-preserving motion planning： Enabling static routing in UAV networks［J］. ACM Transactions on Sensor Networks， 2024， 20（1）： 1-39.
[22]	ALAM M M， MOH S. Joint topology control and routing in a UAV swarm for crowd surveillance［J］. Journal of Network and Computer Applications， 2022， 204： 103427.
[23]	YOO T， LEE S， YOO K， et al. Reinforcement learning based topology control for UAV networks［J］. Sensors， 2023， 23（2）： 921.
[24]	WANG H J， JIANG B， ZHAO H T， et al. Joint resource allocation on slot， space and power towards concurrent transmissions in UAV ad hoc networks［J］. IEEE Transactions on Wireless Communications， 2022， 21（10）： 8698-8712.
[25]	ZHUANG X Y， WU J Q， WU H J， et al. Joint optimization of model inferencing and task offloading for MEC-empowered large vision model services［C］∥ IEEE INFOCOM 2025-IEEE Conference on Computer Communications. Piscataway： IEEE Press， 2025： 1-10.
[26]	GE L H， LIANG X， ZHANG H， et al. Joint resource allocation and trajectory design for resilient multi-UAV communication networks［J］. IEEE Wireless Communications Letters， 2024， 13（4）： 994-998.
[27]	DE ABREU N M M. Old and new results on algebraic connectivity of graphs［J］. Linear Algebra and Its Applications， 2007， 423（1）： 53-73.
[28]	ABDALLAH A E， ABDALLAH E E. A survey on geographical based topology control for ad hoc networks［J］. Procedia Computer Science， 2023， 220： 429-435.
[29]	ALAM M M， ARAFAT M Y， MOH S， et al. Topology control algorithms in multi-unmanned aerial vehicle networks： An extensive survey［J］. Journal of Network and Computer Applications， 2022， 207： 103495.

Resilient reconfiguration of FANETs for network capacity maximization

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