Networked ISAC Systems Toward Low-Altitude Wireless Networks: Joint Optimization of Multi-UAV Deployment and Beamforming Design

MAO Weihao; LU Yang; FAN Dian; WANG Mintian; YANG Dong; AI Bo

doi:10.12263/DZXB.20260121

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Networked ISAC Systems Toward Low-Altitude Wireless Networks: Joint Optimization of Multi-UAV Deployment and Beamforming Design

Integrated Sensing and Communications Towards Low\-Altitude Economy | 更新时间：2026-10-16

- Networked ISAC Systems Toward Low-Altitude Wireless Networks: Joint Optimization of Multi-UAV Deployment and Beamforming Design
- ACTA ELECTRONICA SINICA Vol. 54, Issue 6, Pages: 2570-2582(2026)
- 作者机构：
  
  1.北京交通大学计算机科学与技术学院，北京 100044
  2.中国信息通信研究院，北京 100191
  3.广东城际铁路运营有限公司，广东广州 511458
  4.北京交通大学电子信息工程学院，北京 100044
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China(62571023);Beijing Natural Science Foundation under Grant(L242086)
- DOI：10.12263/DZXB.20260121
  CLC： TP393;TN915
- Received：27 February 2026，
  
  Accepted：30 March 2026，
  
  Published：25 June 2026
- 稿件说明：
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毛炜昊, 陆杨, 范典, 等. 面向低空无线网络的网络化ISAC：无人机集群部署和波束赋形协同优化[J]. 电子学报, 2026, 54(06): 2570-2582.

MAO Weihao, LU Yang, FAN Dian, et al. Networked ISAC Systems Toward Low-Altitude Wireless Networks: Joint Optimization of Multi-UAV Deployment and Beamforming Design[J]. Acta Electronica Sinica, 2026, 54(06): 2570-2582.
毛炜昊, 陆杨, 范典, 等. 面向低空无线网络的网络化ISAC：无人机集群部署和波束赋形协同优化[J]. 电子学报, 2026, 54(06): 2570-2582. DOI：10.12263/DZXB.20260121

MAO Weihao, LU Yang, FAN Dian, et al. Networked ISAC Systems Toward Low-Altitude Wireless Networks: Joint Optimization of Multi-UAV Deployment and Beamforming Design[J]. Acta Electronica Sinica, 2026, 54(06): 2570-2582. DOI：10.12263/DZXB.20260121

摘要

为支撑低空经济（Low-Altitude Economy，LAE）规模化、常态化运行需求，解决低空无线网络（Low-Altitude Wireless Network，LAWN）在复杂空域环境中通信可靠性不足、目标感知精度有限、通信与感知资源相互制约等核心技术瓶颈，提出一种基于网络化通感一体化（Integrated Sensing And Communication，ISAC）的LAWN系统框架。该系统依托无人机（Unmanned Aerial Vehicle，UAV）集群作为空中灵活部署节点，协同承担地面多信息用户的通信服务与空中多感知目标的感知任务，通过雷达接收机实现对感知目标回波信号的接收与处理，构建通信服务与环境感知深度融合的一体化LAWN。以系统用户和速率最大化为优化目标，综合雷达感知信干噪比约束、用户通信信干噪比约束及单架UAV最大发射功率限制，建立发射波束赋形、接收波束赋形及UAV部署位置的联合优化问题。针对原问题的非凸性，设计一种两阶段交替迭代优化算法，通过解耦耦合变量将原问题分解为波束赋形子问题与UAV部署子问题，实现分步迭代寻优。对于波束赋形子问题，采用半正定松弛技术对该子问题进行简化并基于柯西—施瓦茨不等式推导得到接收波束赋形的最优闭式解。在此基础上，提出一个基于连续凸近似的算法处理该子问题，实现发射与接收波束赋形的联合优化。对于UAV部署子问题，通过引入辅助变量将原非凸目标函数等价变换为凸函数，进而设计基于信赖域的迭代求解算法解决该子问题，得到UAV部署位置。理论分析所提基于连续凸近似算法和基于信赖域算法的收敛性、最优性和计算复杂度。仿真结果从单UAV和多UAV集群两种典型场景出发，验证了所提两阶段交替迭代优化算法的有效性与优越性。在单UAV场景下，算法迭代结果与遍历搜索获得的全局最优解基本一致，体现了所提算法的最优性。在多UAV集群场景中，相较于传统UAV中心部署方案和最小均方误差预编码方案，所提算法在用户和速率上取得了显著的性能增益。

Abstract

To support the large-scale and regular operation of the low-altitude economy (LAE)

and address the critical technical bottlenecks of low communication reliability

limited target sensing accuracy

and mutual resource constraints between communication and sensing in low-altitude wireless networks (LAWNs) under complicated airspace environments

this paper proposes a novel LAWN system framework based on networked integrated sensing and communication (ISAC). The proposed framework employs multiple unmanned aerial vehicles (UAVs) as flexible aerial nodes to cooperatively provide communication services for multiple ground information users and perform sensing tasks for multiple aerial targets

where a dedicated radar receiver is adopted to acquire and process echo signals from sensing targets

constructing an integrated LAWN that deeply merges communication services and environmental sensing. To maximize the sum rate of users

we jointly optimize the transmit beamformers

receive beamformers

and UAV deployment positions under the constraints of radar signal-to-interference-plus-noise ratio (SINR)

communication SINR

and transmit power budget of each UAV. To handle the non-convex problem

a two-stage alternating optimization (AO)-based algorithm is proposed

which decouples the original problem into the beamforming design subproblem and the UAV deployment subproblem. For the beamforming design subproblem

semidefinite relaxation is applied to simplify the subproblem

and the optimal closed-form expressions for the receive beamformers are derived via the Cauchy-Schwarz inequality

followed by a successive convex approximation (SCA)-based algorithm to handle the reformulated subproblem. For the UAV deployment subproblem

we introduce auxiliary variables to equivalently transform the non-convex objective function into a convex form

followed by a trust-region-based algorithm to obtain the UAV deployment positions. We theoretically analyze the convergence

optimality

and computational complexity of the proposed SCA-based and trust-region-based algorithms. Simulation results validate the effectiveness and superiority of the proposed two stage AO-based algorithm in both single-UAV and multi-UAV scenarios. In the single-UAV case

the proposed algorithm closely matches the global optimal solution obtained by exhaustive search

demonstrating the optimality of the proposed algorithm. In the multi-UAV case

the proposed algorithm achieves significant sum rate performance gains compared with the UAV central deployment design and minimum mean square error precoding design.

关键词

Keywords

references

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