面向低空无人机集群的ILAC网络中联合用户关联与资源优化方法

杨卓佳; 苏伟; 杨斌; 马煜东; 王文迪

doi:10.12263/DZXB.20260403

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面向低空无人机集群的ILAC网络中联合用户关联与资源优化方法

学术论文 | 更新时间：2026-10-16

- 面向低空无人机集群的ILAC网络中联合用户关联与资源优化方法
- Joint User Association and Resource Optimization for Low-Altitude UAV Swarm-Based ILAC Networks
- 电子学报 2026年54卷第6期页码：2676-2691
- 作者机构：
  
  1.北京交通大学电子信息工程学院，北京 100044
  2.滁州学院计算机与信息工程学院，安徽滁州 239000
  3.北京航空航天大学人工智能学院，北京 100083
- 作者简介：
  
  杨卓佳女。现为北京交通大学移动专网网络国家工程研究中心博士研究生。主要研究方向为低空智联网、无人机通信与资源分配等。中国电子学会会员编号：E190202617A。E-mail: 23111050@bjtu.edu.cn
  苏伟男。现为北京交通大学电子与信息工程学院教授，移动专网网络国家工程研究中心副主任。主要研究方向为新一代信息网络理论与关键技术等。E-mail: wsu@bjtu.edu.cn
  杨斌男。现为滁州学院计算机与信息工程学院副院长、教授。主要研究方向为网络安全、边缘计算、物联网和无人机网络等。E-mail: ybcup@chzu.edu.cn
  马煜东男。现为北京航空航天大学人工智能学院博士研究生。主要研究方向为基于L波段数字航空通信系统的导航与监视技术。E-mail: yudongma1998@buaa.edu.cn
  王文迪男。现为北京交通大学移动专网网络国家工程研究中心硕士研究生。主要研究方向为无人机通感一体化、低空智联网。E-mail: 24125060@bjtu.edu.cn
- 基金信息：
  
  雄安新区科技创新专项(2025XAGG0055);国家自然科学基金面上项目(62372076)
- DOI：10.12263/DZXB.20260403
  中图分类号： TN929.5;
- 收稿：2026-04-16，
  
  录用：2026-05-05，
  
  纸质出版：2026-06-25
- 稿件说明：
移动端阅览
杨卓佳, 苏伟, 杨斌, 等. 面向低空无人机集群的ILAC网络中联合用户关联与资源优化方法[J]. 电子学报, 2026, 54(06): 2676-2691.

YANG Zhuojia, SU Wei, YANG Bin, et al. Joint User Association and Resource Optimization for Low-Altitude UAV Swarm-Based ILAC Networks[J]. Acta Electronica Sinica, 2026, 54(06): 2676-2691.
杨卓佳, 苏伟, 杨斌, 等. 面向低空无人机集群的ILAC网络中联合用户关联与资源优化方法[J]. 电子学报, 2026, 54(06): 2676-2691. DOI：10.12263/DZXB.20260403

YANG Zhuojia, SU Wei, YANG Bin, et al. Joint User Association and Resource Optimization for Low-Altitude UAV Swarm-Based ILAC Networks[J]. Acta Electronica Sinica, 2026, 54(06): 2676-2691. DOI：10.12263/DZXB.20260403

摘要

通信定位一体化（Integrated Localization And Communication，ILAC）网络通过通信与定位的深度融合，能够在提供通信服务的同时实现高精度位置感知，成为未来无线网络发展的重要方向之一。然而，在应急救援场景下，传统依赖地面基站的ILAC网络易受到地面基础设施损毁影响，将面临通信覆盖中断、链路质量恶化和定位精度下降等问题。为此，本文构建了一种低空无人机集群辅助的ILAC网络，为受灾区域用户提供临时与分布式的通信与定位服务，并提出了一种基于分布式架构的无人机关联、频谱与功率联合优化方法，以保障高效的通信与定位服务，支持应急救援等关键任务的顺利执行。首先，本文建立基于双边双向测距的用户定位模型，描述无人机空中锚点与地面用户之间的测距关系及其对定位精度的影响。之后，构建以最大化最小用户吞吐量为目标，定位精度、无人机关联、频谱与功率等为约束的联合优化问题。针对此复杂优化问题，我们将其划分为无人机关联与功率-子信道优化两个子问题。针对第一个子问题，提出三维快速空中锚点选择方法，通过空间划分降低空中锚点搜索空间，实现用户通信接入点与定位锚点的快速关联；针对第二个子问题，提出基于多智能体近端策略优化的联合功率控制与子信道分配算法，利用分布式多智能体学习实现功率与子信道的协同决策。通过与多种基线算法仿真对比，结果表明本文所提出方法能够在保障定位精度约束的同时有效提升最小用户吞吐量，提高用户间通信公平性，并在相同网络场景设置下获得更优的定位性能，从而为应急场景下实现高效通信与可靠定位服务提供有力支持。

Abstract

Integrated localization and communication (ILAC) networks deeply integrate communication and positioning functions

enabling efficient communication transmission services while providing reliable positioning services. Owing to their ability to support both information exchange and positioning perception within a unified wireless network

ILAC networks have emerged as a key direction for the evolution of future wireless systems. However

in emergency rescue scenarios

traditional ILAC networks that rely on terrestrial base stations may become unavailable or severely degraded due to damage to ground infrastructure. Such infrastructure failures can result in communication coverage interruption

link quality deterioration

insufficient localization anchors

and reduced positioning accuracy

making it difficult to continuously support mission-critical services in disaster-affected areas.To address this issue

this paper proposes an unmanned aerial vehicle(UAV)-swarm-enabled ILAC network

in which multiple UAVs are deployed as flexible aerial platforms to provide temporary

distributed

and reconfigurable communication and localization services for ground users in disaster-affected regions. Furthermore

a distributed framework is proposed for the joint optimization method of UAV association

spectrum allocation

and power control to ensure efficient communication and positioning services

thereby supporting the successful execution of critical tasks such as emergency rescue. First

a user positioning model based on double-sided two-way ranging is established to describe the ranging relationship between aerial UAV anchors and ground users

as well as the impact of ranging measurements and anchor selection on positioning accuracy. On this basis

the coupling relationship among communication access

positioning anchor association

spectrum sharing

and transmit power allocation is further characterized. Then

a joint optimization problem is formulated with the objective of maximizing the minimum user throughput

while considering positioning accuracy requirements

UAV association constraints

sub-channel allocation constraints

and power constraints. Since the formulated problem involves both discrete association decisions and continuous resource allocation variables

and the communication and positioning performances are strongly coupled through shared UAV anchor nodes and wireless resources

the problem is highly non-convex and difficult to solve directly by conventional centralized optimization methods. To tackle this complexity

the original problem is decomposed into two subproblems: UAV association

power control

and sub-channel allocation. For the first subproblem

a three-dimensional fast aerial anchor selection method is proposed. By introducing spatial partitioning

the proposed method reduces the search space of candidate aerial anchors and enables rapid association between user communication access points and positioning anchors

thereby lowering the computational complexity of UAV association while preserving positioning performance. For the second subproblem

a multi-agent proximal policy optimization-based joint power control and sub-channel allocation algorithm is proposed. By leveraging distributed multi-agent learning

users can learn coordinated resource allocation policies under local network observations

so as to jointly determine spectrum and power configurations in dynamic multi-user scenarios.Simulation comparisons with multiple baseline algorithms demonstrate that the proposed method can effectively improve the minimum user throughput and enhance communication fairness among users while satisfying positioning accuracy constraints. Moreover

under the same network scenario settings

the proposed method achieves superior positioning performance

indicating its effectiveness in jointly balancing communication efficiency and positioning reliability. These results show that the proposed UAV swarm-enabled ILAC framework provides a feasible and effective solution for supporting efficient communication and reliable positioning services in emergency rescue scenarios.

关键词

Keywords

references

Xiao Zhiqiang , Zeng Yong . An overview on integrated localization and communication towards 6G [J ] . Science China Information Sciences , 2021 , 65 ( 3 ): 131301 . DOI: 10.1007/s11432-020-3218-8 http://dx.doi.org/10.1007/s11432-020-3218-8

Meng Kaitao , Wu Qingqing , Xu Jie , et al . UAV-enabled integrated sensing and communication: Opportunities and challenges [J ] . IEEE Wireless Communications , 2024 , 31 ( 2 ): 97 - 104 . DOI: 10.1109/mwc.131.2200442 http://dx.doi.org/10.1109/mwc.131.2200442

Gao Yuan , Hu Haonan , Zhang Jiliang , et al . On the performance of an integrated communication and localization system: An analytical framework [J ] . IEEE Transactions on Vehicular Technology , 2024 , 73 ( 7 ): 10845 - 10849 . DOI: 10.1109/tvt.2024.3364257 http://dx.doi.org/10.1109/tvt.2024.3364257

Gao Yuan , Du Haoyu , Jiang Zhenwei , et al . A stochastic-geometry-based analytical framework for integrated localization and communication systems [J ] . IEEE Internet of Things Journal , 2025 , 12 ( 18 ): 38710 - 38722 . DOI: 10.1109/jiot.2025.3586979 http://dx.doi.org/10.1109/jiot.2025.3586979

Gao Junyuan , Zhu Weifeng , Zhang Shuowen , et al . Integrated massive communication and target localization in 6G cell-free networks [PP/OL ] . V1. arXiv ( 2025-10-16 )[ 2026-04-16 ] . https://doi.org/10.48550/arXiv.2510.14281 https://doi.org/10.48550/arXiv.2510.14281 .

Eskandari M , Savkin A V , Deghat M . RIS-enabled energy efficient integrated localization and communication for autonomous vehicles: Complementary to visual SLAM [J ] . IEEE Transactions on Green Communications and Networking , 2026 , 10 : 332 - 343 . DOI: 10.1109/tgcn.2025.3582071 http://dx.doi.org/10.1109/tgcn.2025.3582071

Peng Xingyu , Hu Xiaoling , Gao Jiabao , et al . Integrated localization and communication for IRS-assisted multi-user mmWave MIMO systems [J ] . IEEE Transactions on Communications , 2024 , 72 ( 8 ): 4725 - 4740 . DOI: 10.1109/tcomm.2024.3379399 http://dx.doi.org/10.1109/tcomm.2024.3379399

Adhikary A , Munir M S , Raha A D , et al . Integrated sensing, localization, and communication in holographic MIMO-enabled wireless network: A deep learning approach [J ] . IEEE Transactions on Network and Service Management , 2024 , 21 ( 1 ): 789 - 809 . DOI: 10.1109/tnsm.2023.3292269 http://dx.doi.org/10.1109/tnsm.2023.3292269

Chen Xu , He Xinxin , Feng Zhiyong , et al . Joint localization and communication enhancement in uplink integrated sensing and communications system with clock asynchronism [J ] . IEEE Journal on Selected Areas in Communications , 2024 , 42 ( 10 ): 2659 - 2673 . DOI: 10.1109/jsac.2024.3414625 http://dx.doi.org/10.1109/jsac.2024.3414625

Jiang Fan , Abrardo A , Keykhosravi K , et al . Two-timescale transmission design and RIS optimization for integrated localization and communications [J ] . IEEE Transactions on Wireless Communications , 2023 , 22 ( 12 ): 8587 - 8602 . DOI: 10.1109/twc.2023.3264559 http://dx.doi.org/10.1109/twc.2023.3264559

Yang Helin , Zhang Sheng , Alphones A , et al . An advanced integrated visible light communication and localization system [J ] . IEEE Transactions on Communications , 2023 , 71 ( 12 ): 7149 - 7162 . DOI: 10.1109/tcomm.2023.3309823 http://dx.doi.org/10.1109/tcomm.2023.3309823

Li Biwei , Wang Xianbin , Xin Yan , et al . Value of service maximization in integrated localization and communication system through joint resource allocation [J ] . IEEE Transactions on Communications , 2023 , 71 ( 8 ): 4957 - 4971 . DOI: 10.1109/tcomm.2023.3280212 http://dx.doi.org/10.1109/tcomm.2023.3280212

Yang Zheyuan , Bi Suzhi , Zhang Y A . Deployment optimization of dual-functional UAVs for integrated localization and communication [J ] . IEEE Transactions on Wireless Communications , 2023 , 22 ( 12 ): 9672 - 9687 . DOI: 10.1109/twc.2023.3273159 http://dx.doi.org/10.1109/twc.2023.3273159

Bi Suzhi , Zhuo Zhenpeng , Lin Xiaohui , et al . Physical-environment-map-aided 3-D deployment optimization for UAV-assisted integrated localization and communication in urban areas [J ] . IEEE Internet of Things Journal , 2024 , 11 ( 9 ): 15490 - 15503 . DOI: 10.1109/jiot.2023.3346892 http://dx.doi.org/10.1109/jiot.2023.3346892

Wang Li , Li Ruoguang , Xu Lianming , et al . Aerial-ground cooperative vehicular networks for emergency integrated localization and communication [J ] . IEEE Network , 2023 , 37 ( 4 ): 323 - 330 . DOI: 10.1109/mnet.015.2300117 http://dx.doi.org/10.1109/mnet.015.2300117

Wang Zijie , Liu Rongke , Liu Qirui , et al . Feasibility study of UAV-assisted anti-jamming positioning [J ] . IEEE Transactions on Vehicular Technology , 2021 , 70 ( 8 ): 7718 - 7733 . DOI: 10.1109/tvt.2021.3090403 http://dx.doi.org/10.1109/tvt.2021.3090403

Wang Zijie , Liu Rongke , Liu Qirui , et al . Energy-efficient data collection and device positioning in UAV-assisted IoT [J ] . IEEE Internet of Things Journal , 2020 , 7 ( 2 ): 1122 - 1139 . DOI: 10.1109/jiot.2019.2952364 http://dx.doi.org/10.1109/jiot.2019.2952364

Yang Jimin , Lee J , Lim J . Joint placement and communication optimization of uav base stations in GPS-denied environments [J ] . Journal of Communications and Networks , 2024 , 26 ( 5 ): 490 - 501 . DOI: 10.23919/jcn.2024.000056 http://dx.doi.org/10.23919/jcn.2024.000056

Zhu Qian , Liu Rongke , Wang Zijie , et al . Sensing-communication co-design for UAV swarm-assisted vehicular network in perspective of Doppler [J ] . IEEE Transactions on Vehicular Technology , 2024 , 73 ( 2 ): 2578 - 2592 .

Zhu Qian , Liu Rongke , Liu Qirui , et al . Resource allocation for UAV swarm-assisted green ISAC networks via multi-agent RL [J ] . IEEE Transactions on Green Communications and Networking , 2025 , 9 ( 3 ): 1354 - 1367 . DOI: 10.1109/tgcn.2024.3487995 http://dx.doi.org/10.1109/tgcn.2024.3487995

高思华 , 李军辉 , 李建伏 , 等 . 面向公平性数据采集和能量补充的无人机路径规划算法研究 [J ] . 电子学报 , 2024 , 52 ( 11 ): 3699 - 3710 .

Gao Sihua , Li Junhui , Li Jianfu , et al . Research on UAV path planning algorithm for fairness data collection and energy supplement [J ] . Acta Electronica Sinica , 2024 , 52 ( 11 ): 3699 - 3710 . (in Chinese)

Frattasi S , Della Rosa F . Mobile positioning and tracking: From conventional to cooperative techniques [M ] . New York : John Wiley & Sons , 2017 . DOI: 10.1002/9781119068846.ch12 http://dx.doi.org/10.1002/9781119068846.ch12

Zekavat S A R , Buehrer R M . Handbook of position location: Theory, practice, and advances [M ] . 2nd ed . Hoboken : Wiley , 2018 . DOI: 10.1002/9781119434610 http://dx.doi.org/10.1002/9781119434610

Dong Fuwang , Liu Fan , Cui Yuanhao , et al . Sensing as a service in 6G perceptive networks: A unified framework for ISAC resource allocation [J ] . IEEE Transactions on Wireless Communications , 2023 , 22 ( 5 ): 3522 - 3536 . DOI: 10.1109/twc.2022.3219463 http://dx.doi.org/10.1109/twc.2022.3219463

Kaune R , Hörst J , Koch W . Accuracy analysis for TDOA localization in sensor networks [C ] // 14th International Conference on Information Fusion . Piscataway : IEEE , 2011 : 1 - 8 .

Del Peral-Rosado J A , I Castillo R E , Mıguez-Sanchez J , et al . Performance analysis of hybrid GNSS and LTE Localization in urban scenarios [C ] // 2016 8th ESA Workshop on Satellite Navigation Technologies and European Workshop on GNSS Signals and Signal Processing . Piscataway : IEEE , 2016 : 1 - 8 . DOI: 10.1109/navitec.2016.7849332 http://dx.doi.org/10.1109/navitec.2016.7849332

陈平平 , 张旭 , 谢肇鹏 , 等 . 基于多智能体近端策略优化的多信道动态频谱接入 [J ] . 电子学报 , 2024 , 52 ( 6 ): 1824 - 1831 .

Chen Pingping , Zhang Xu , Xie Zhaopeng , et al . Multi-channel dynamic spectrum access based on multi-agent proximal policy optimization [J ] . Acta Electronica Sinica , 2024 , 52 ( 6 ): 1824 - 1831 . (in Chinese)

Schulman J , Wolski F , Dhariwal P , et al . Proximal policy optimization algorithms [PP/OL ] . V2. arXiv ( 2017-08-28 )[ 2026-04-16 ] . https://doi.org/10.48550/arXiv.1707.06347 https://doi.org/10.48550/arXiv.1707.06347 .

Cui Jingjing , Liu Yuanwei , Nallanathan A . Multi-agent reinforcement learning-based resource allocation for UAV networks [J ] . IEEE Transactions on Wireless Communications , 2020 , 19 ( 2 ): 729 - 743 . DOI: 10.1109/twc.2019.2935201 http://dx.doi.org/10.1109/twc.2019.2935201

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