通感一体化赋能的低空无人机数字孪生同步性能联合优化

代陈; 朱靖怡; 左益平; 曹浩彤; 肖甫

doi:10.11999/JEIT261138

通感一体化赋能的低空无人机数字孪生同步性能联合优化

doi: 10.11999/JEIT261138 cstr: 32379.14.JEIT261138

代陈^{1, 2,},
朱靖怡^{1, 2},
左益平^{1, 2},
曹浩彤³,
肖甫^{1, 2, ,}

1.
南京邮电大学计算机学院软件学院网络空间安全学院南京 210023
2.
江苏省物联网智能感知与计算重点实验室南京 210023
3.
南京邮电大学通信与信息工程学院南京 210003

基金项目: 国家自然科学基金青年科学基金项目(62402232, 62301279，62501306)，国家自然科学基金杰出青年科学基金项目 (62125203)，江苏省自然科学基金青年科学基金项目(BK20240623)，江苏省特聘教授计划项目

详细信息

作者简介:
代陈：男，讲师，硕士生导师，研究方向为通感一体化、数字孪生、无人机通信网络，邮箱daichen@njupt.edu.cn

朱靖怡：女，硕士生，研究方向为通感一体化、数字孪生、无人机通信网络

左益平：女，讲师，硕士生导师，研究方向为通感一体化、无人机通信网络

曹浩彤：男，教授，硕士生导师，研究方向为6G智能通信与网络、低空智简通信

肖甫：男，教授，博士生导师，研究方向为物联网感知计算、通感一体化

通讯作者:
肖甫　xiaof@njupt.edu.cn

中图分类号: XXXXXXXXXXX
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出版历程
- 修回日期: 2026-10-16
- 录用日期: 2026-10-16
- 网络出版日期: 2026-10-22

Joint Optimization of Digital Twin Synchronization Performance for ISAC-Enabled Low-Altitude UAV Systems

DAI Chen^{1, 2
,},
ZHU Jingyi^{1, 2},
ZUO Yiping^{1, 2},
CAO Haotong³,
XIAO Fu^{1, 2
, ,}

1.
School of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
2.
Jiangsu Provincial Key Laboratory of Internet of Things Intelligent Perception and Computing, Nanjing 210023, China
3.
College of Communication and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, China

摘要

摘要: 针对低空无人机数字孪生(Digital Twin, DT)系统中感知与通信分离设计导致频谱资源利用率较低、状态更新时延较大以及虚实映射精度受限的问题，提出一种通感一体化(Integrated Sensing and Communication，ISAC)赋能的数字孪生同步性能联合优化方法。首先，构建由物理s层、ISAC模块和DT层组成的低空无人机数字孪生闭环架构，并建立通信时延、感知误差和数字孪生同步性能模型。在此基础上，分析带宽、发射功率与无人机轨迹之间的耦合关系，以数字孪生映射误差和更新时间的加权和最小为目标，建立长期联合优化问题。考虑到无人机轨迹控制属于长期序列决策，而带宽和发射功率配置需要在单个时隙内完成，设计一种由面向数字孪生的近端策略优化和交替资源优化模块组成的协同求解方法。其中，DT-aware PPO(Proximal Policy Optimization, PPO)用于优化无人机的长期飞行轨迹，资源优化模块根据当前轨迹和信道状态配置带宽与发射功率，并将数字孪生同步性能反馈给轨迹控制模块。在给定仿真参数下，与非ISAC PPO方案相比，所提方法将数字孪生映射误差由2.009降低至1.084，将平均通信时延由13.102 s降低至3.200 s。与随机轨迹、资源均分等资源分配及典型路径规划算法相比，所提方法能够进一步降低系统综合代价，并在动态扰动下保持较好的决策效率和稳定性。
- 低空无人机 /
- 数字孪生 /
- 通感一体化 /
- 轨迹控制 /
- 资源分配
Abstract: Objective With the rapid development of low-altitude intelligent networks, uncrewed aerial vehicles (UAVs) are increasingly used in inspection, logistics, and emergency response. These applications require continuous state sensing, wireless transmission, and interaction with digital twin (DT) models to support real-time decision-making. However, existing UAV DT systems generally separate sensing and communication, resulting in inefficient resource utilization, increased update latency, and limited synchronization accuracy. Integrated Sensing and Communication (ISAC) enables sensing and communication to share spectrum, transmit power, and hardware resources. Nevertheless, the coupling among UAV trajectory control, ISAC resource allocation, and DT synchronization performance remains insufficiently investigated. Therefore, this paper investigates the joint optimization of DT synchronization performance in ISAC-enabled low-altitude UAV systems. Methods An ISAC-enabled low-altitude UAV DT architecture consisting of the physical layer, ISAC module, and DT layer deployed on a mobile edge computing (MEC) server is established. Communication latency, sensing error, and DT mapping error models are developed to characterize the coupling among bandwidth allocation, transmit power, channel conditions, UAV trajectory, and synchronization performance. A joint optimization problem is formulated to minimize the long-term average DT mapping error subject to UAV mobility, wireless resource, and synchronization delay constraints. To solve the resulting non-convex problem, a collaborative framework integrating Digital Twin-aware Proximal Policy Optimization (DT-aware PPO) and Alternating Optimization (AO) is proposed. DT-aware PPO optimizes long-term UAV trajectory decisions, while the AO-based resource optimization module dynamically allocates bandwidth and transmit power at each time slot. The resulting DT synchronization performance is fed back to the PPO agent to coordinate long-term trajectory control and short-term resource allocation. Results and Discussions Simulation experiments are conducted on a MATLAB-based low-altitude UAV digital twin platform under a dynamic inspection scenario. Compared with the Non-ISAC-PPO scheme, the proposed method reduces the DT mapping error from 2.009 to 1.084 and the average communication delay from 13.102 s to 3.200 s (Fig. 4). Compared with the Random Trajectory and Equal Resource Allocation schemes, the proposed method achieves lower system objective value, synchronization delay, and DT mapping error by jointly optimizing UAV trajectory and wireless resources (Fig. 5). The average reward gradually converges during training, indicating stable learning of the DT-aware PPO policy (Fig. 6). Compared with Non-ISAC A*, ISAC-Greedy, and ISAC-RRT*, the proposed method achieves lower system cost, faster decision making, and improved performance recovery under dynamic disturbances (Fig. 7). Parameter sensitivity analysis further shows that increasing bandwidth and transmit power improves DT synchronization performance, whereas excessive task arrival rates degrade performance due to queue accumulation (Fig. 8). Conclusions This paper proposes a joint optimization method for DT synchronization in ISAC-enabled low-altitude UAV systems. A unified communication-sensing-DT coupling model is established to characterize the interactions among UAV trajectory, wireless resource allocation, communication latency, sensing accuracy, and DT synchronization performance. A collaborative framework integrating DT-aware PPO and AO-based resource allocation is developed for long-term trajectory control and short-term resource allocation. Simulation results demonstrate that the proposed method effectively reduces DT mapping error and communication latency while maintaining stable learning and a certain degree of adaptability to dynamic disturbances.
- Low-altitude UAV /
- Digital twin (DT) /
- Integrated sensing and communication (ISAC) /
- Trajectory optimization /
- Resource allocation

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图 1 ISAC赋能低空无人机数字孪生系统闭环优化框架

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图 2 数字孪生系统通信模型示意图

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图 3 数字孪生系统协同感知模型示意图

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图 4 ISAC-PPO与无ISAC-PPO的无人机飞行轨迹及数字孪生同步性能对比

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图 5 不同优化方案下数字孪生同步性能随时隙的变化

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图 6 不同算法的平均奖励收敛曲线对比

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图 7 不同算法的系统性能、决策效率及动态抗扰能力对比

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图 8 关键系统参数对数字孪生同步性能的影响

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表 1 DT-aware PPO与资源优化协同算法

输入：系统参数、Actor网络参数 $ \theta $、Critic网络参数 $ \phi $、最大　训练回合数E。
输出：优化后的无人机控制策略。
(1) 初始化Actor网络、Critic网络及资源优化模块参数；
(2) for episode = 1,2,···,E do
(3) 　初始化无人机状态 $ {s}_{t}=\{{\boldsymbol{q}}_{t},{\boldsymbol{v}}_{t},{\boldsymbol{h}}_{t},{\boldsymbol{L}}_{t}\} $；
(4) 　for t = 1,2,···,T do
(5) 　　根据策略$ {\pi }_{\theta }({s}_{t}) $生成控制动作$ {a}_{t} $，并执行约束投影满足　　　　加速度限制；
(6) 　　根据无人机运动模型更新位置$ {\boldsymbol{q}}_{t+1}=f\left({\boldsymbol{q}}_{t},{\boldsymbol{a}}_{t}\right) $；
(7) 　　固定无人机轨迹$ {\boldsymbol{q}}_{t} $，求解通信感知资源优化问题(式14)；
(8) 　　根据资源配置结果计算DT同步性能 $ {E}^{\text{DT}}(t) $ 与　　　　 $ {T}^{\text{DT}}(t) $；
(9) 　　根据DT同步反馈计算奖励(式18)；
(10) 　　更新Actor-Critic网络。
(11) 　 end for
(12) end for
(13) return优化后的飞行控制策略

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表 2 仿真参数

参数名称	符号	数值	参数名称	符号	数值
无人机数量	N	3	时隙长度	$ {\varDelta }t $	1 s
无人机飞行高度	H	100 m	任务到达率	λ	默认1.0
无人机最大飞行速度	$ {V}_{\max } $	30 m/s	本地感知误差方差	$ \sigma _{\text{local}}^{2} $	0.05
无人机最大加速度^[13]	$ {a}_{\max } $	5 m/$ {s}^{2} $	状态漂移率系数	$ {\alpha }_{i} $	0.1
载波频率[7]	$ {f}_{c} $	2.4GHz	最大允许同步时延	$ {T}_{\max } $	8s
参考距离信道增益[1]	$ {\beta }_{0} $	-30dB	单个任务数据量	$ {D}_{\text{task}} $	1 Mbit
路径损耗指数[14]	ζ	2.2	正惩罚系数	$ {C}_{\text{penalty}} $	100
噪声功率谱密度[14]	$ {N}_{0} $	–174 dBm/Hz	PPO训练参数	$ \gamma ,{\lambda }_{\text{GAE}},{\epsilon }_{\text{clip}} $	0.99，0.95，0.2
系统总带宽	B	默认10 MHz	系统最大发射功率	$ {P}_{\max } $	默认0.8 W

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