Research on Joint Allocation of Multi-Dimensional Resources for UAV-Ground Base Station Coordinated Integrated Sensing and Communication Systems

ZENG Haiyong; HUANG Yuanyan; WEI Zhongxiang; JIANG Chunsheng; ZHU Xu; CAO Jie

doi:10.12263/DZXB.20260170

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Research on Joint Allocation of Multi-Dimensional Resources for UAV-Ground Base Station Coordinated Integrated Sensing and Communication Systems

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

- Research on Joint Allocation of Multi-Dimensional Resources for UAV-Ground Base Station Coordinated Integrated Sensing and Communication Systems
- ACTA ELECTRONICA SINICA Vol. 54, Issue 6, Pages: 2583-2597(2026)
- 作者机构：
  
  1.广西类脑计算与智能芯片重点实验室（广西师范大学），广西桂林 541004
  2.广西师范大学电子与信息工程学院/集成电路学院，广西桂林 541004
  3.同济大学电子与信息工程学院，上海 200092
  4.哈尔滨工业大学（深圳）信息科学与技术学院，广东深圳 518000
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China(62301172;62401182;U24A20296);Guangdong International Science and Technology Cooperation Program(2025A0505020024)
- DOI：10.12263/DZXB.20260170
  CLC： TN929.5;TN915.0
- Received：08 April 2026，
  
  Accepted：08 May 2026，
  
  Published：25 June 2026
- 稿件说明：
移动端阅览
曾海勇, 黄媛艳, 魏忠祥, 等. 面向无人机-地面基站协同通感一体化系统的多维资源联合分配研究[J]. 电子学报, 2026, 54(06): 2583-2597.

ZENG Haiyong, HUANG Yuanyan, WEI Zhongxiang, et al. Research on Joint Allocation of Multi-Dimensional Resources for UAV-Ground Base Station Coordinated Integrated Sensing and Communication Systems[J]. Acta Electronica Sinica, 2026, 54(06): 2583-2597.
曾海勇, 黄媛艳, 魏忠祥, 等. 面向无人机-地面基站协同通感一体化系统的多维资源联合分配研究[J]. 电子学报, 2026, 54(06): 2583-2597. DOI：10.12263/DZXB.20260170

ZENG Haiyong, HUANG Yuanyan, WEI Zhongxiang, et al. Research on Joint Allocation of Multi-Dimensional Resources for UAV-Ground Base Station Coordinated Integrated Sensing and Communication Systems[J]. Acta Electronica Sinica, 2026, 54(06): 2583-2597. DOI：10.12263/DZXB.20260170

摘要

针对低空经济背景下6G网络通信与感知融合的基础技术需求，现有固定多基站协同通感一体化架构灵活性受限，无人机通感一体化研究多为单基站独立运行，缺少与地面基站的协同配合，且空地协同场景下轨迹规划、用户调度与功率分配存在强耦合关系，优化模型非凸且求解复杂。为此，本文提出无人机-地面基站协同通感一体化传输架构及其多维资源联合分配和无人机轨迹优化方案。该架构利用无人机灵活机动特性，与地面基站构建空地协同通信和感知一体化机制，同步完成边缘用户通信服务与移动目标感知任务。本文以最小化系统总发射功率为优化目标，在满足用户信干噪比与目标定位克拉美罗下界双重约束的前提下，建立空地协同场景下包含无人机轨迹、用户调度、功率的多维资源联合优化模型。针对该非凸优化问题中离散调度、连续功率、动态轨迹三类变量深度耦合、难以直接联合求解的特点，本文提出了基于深度强化学习的双层优化框架。外层采用深度确定性策略梯度算法，依托连续动作空间优势迭代优化无人机飞行轨迹，适配高移动性带来的动态拓扑变化；内层利用半正定松弛技术，将复杂非凸资源分配问题转化为可高效求解的半正定规划形式，快速获得给定无人机位置下的联合用户调度和功率分配近似最优解。本文将内层资源优化得到的最小发射功率构造为奖励函数反馈至外层，实现轨迹策略与资源分配的双向联动更新。该分层耦合设计既规避了全强化学习方案动作空间冗余、约束嵌入困难、训练波动明显的缺陷，又弥补了传统数学规划方法对初始点敏感、易陷入局部最优的不足，能够在动态场景下持续维持可行优化域。仿真结果表明，所提空地协同通感一体化方案相较非协同方法可实现30%~36%的功耗节约，同时本文提出的双层优化方案相较于全深度强化学习、固定轨迹方案功耗分别降低39%、17%。研究结果可为低空经济下空地协同通感一体化系统的轨迹设计与资源配置提供理论依据和技术参考。

Abstract

In view of the fundamental technical requirements for the integration of communication and sensing in 6G networks under the background of low-altitude economy

the existing fixed multi-base-station integrated sensing and communication (ISAC) architectures suffer from limited flexibility. Most current unmanned aerial vehicle (UAV)-enabled ISAC studies focus on independent operation without cooperation with ground-based stations. In addition

trajectory planning

user scheduling

and power allocation exhibit strong coupling in air-ground coordinated scenarios

resulting in non-convex and intractable optimization models. To address these issues

this paper proposes an air-ground coordinated ISAC transmission architecture consisting of UAVs and ground base stations

along with a joint multi-dimensional resource allocation scheme. Leveraging the flexible mobility of UAVs

the constructed architecture establishes an air-ground coordinated communication and sensing mechanism to simultaneously provide communication services for edge users and accomplish sensing tasks for moving targets. With the objective of minimizing the total system transmit power

a multi-dimensional joint optimization model involving UAV trajectory

user scheduling

and power allocation is formulated under the constraints of user signal-to-interference-plus-noise ratio (SINR) and target localization Cramér-Rao lower bound (CRLB). To handle the deeply coupled discrete scheduling

continuous power and dynamic trajectory variables that are difficult to solve jointly

a deep reinforcement learning-based two-layer optimization framework is proposed. In the outer layer

a deep deterministic policy gradient (DDPG) algorithm is adopted to iteratively optimize UAV flight trajectories in continuous action space

adapting to dynamic topological variations caused by high mobility. In the inner layer

semidefinite relaxation (SDR) transforms the non-convex resource allocation problem into a tractable semidefinite programming form

so as to efficiently obtain near-optimal solutions of joint user scheduling and power allocation under fixed UAV positions. The minimal transmit power obtained from inner-layer optimization is constructed as a reward function and fed back to the outer layer

realizing bidirectional interactive updating between trajectory strategies and resource allocation. This hierarchical coupling design avoids the drawbacks of pure reinforcement learning methods

such as redundant action space

difficult constraint embedding and unstable training. Moreover

it alleviates the sensitivity to initial values and local optimum defects of traditional mathematical optimization methods

maintaining feasible optimization domains in dynamic environments. Simulation results demonstrate that the proposed air-ground coordinated ISAC scheme achieves 30%~36% power consumption reduction compared with non-coordinated methods. Furthermore

the proposed two-layer optimization scheme reduces power consumption by 39% and 17% compared with the pure deep reinforcement learning scheme and fixed-trajectory scheme

respectively. This research provides a theoretical basis and technical reference for trajectory design and resource configuration of air-ground coordinated ISAC systems in low-altitude economy scenarios.

关键词

Keywords

references

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