System Cost Function Optimization-Based Data Scheduling, Radar Precoding and Flight Trajectory Design for UAV-Assisted Integrated Sensing and Communication Systems

CHAI Rong; WANG Bing-yan; SUN Rui-jin; JING Xiao-rong

doi:10.12263/DZXB.20240656

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System Cost Function Optimization-Based Data Scheduling, Radar Precoding and Flight Trajectory Design for UAV-Assisted Integrated Sensing and Communication Systems

Intelligent Vision Algorithms for Unmanned Systems | 更新时间：2025-12-08

- System Cost Function Optimization-Based Data Scheduling, Radar Precoding and Flight Trajectory Design for UAV-Assisted Integrated Sensing and Communication Systems
- ACTA ELECTRONICA SINICA Vol. 53, Issue 3, Pages: 744-753(2025)
- 作者机构：
  
  1.重庆邮电大学通信与信息工程学院，重庆 400065
  2.西安电子科技大学综合业务网理论及关键技术国家重点实验室，陕西西安 710126
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China(62271097)
- DOI：10.12263/DZXB.20240656
  CLC： TN915;
- Received：11 July 2024，
  
  Revised：2025-02-20，
  
  Published：25 March 2025
- 稿件说明：
移动端阅览
柴蓉, 王丙燕, 孙瑞锦, 等. 基于系统成本函数优化的无人机辅助通感一体化系统数据调度、雷达预编码及飞行轨迹优化方法[J]. 电子学报, 2025, 53(03): 744-753.

CHAI Rong, WANG Bing-yan, SUN Rui-jin, et al. System Cost Function Optimization-Based Data Scheduling, Radar Precoding and Flight Trajectory Design for UAV-Assisted Integrated Sensing and Communication Systems[J]. Acta Electronica Sinica, 2025, 53(03): 744-753.
柴蓉, 王丙燕, 孙瑞锦, 等. 基于系统成本函数优化的无人机辅助通感一体化系统数据调度、雷达预编码及飞行轨迹优化方法[J]. 电子学报, 2025, 53(03): 744-753. DOI：10.12263/DZXB.20240656

CHAI Rong, WANG Bing-yan, SUN Rui-jin, et al. System Cost Function Optimization-Based Data Scheduling, Radar Precoding and Flight Trajectory Design for UAV-Assisted Integrated Sensing and Communication Systems[J]. Acta Electronica Sinica, 2025, 53(03): 744-753. DOI：10.12263/DZXB.20240656

摘要

本文针对多天线无人机（Unmanned Aerial Vehicle，UAV）辅助的通信感知一体化场景，综合考虑目标感知及用户通信性能，定义系统成本函数为系统能耗与用户速率的加权和，将通信感知调度、感知预编码设计以及UAV飞行轨迹优化问题建模为一个满足约束条件的系统成本函数优化问题.由于所建模优化问题为一个变量高度耦合且非凸的混合整型优化问题，难以直接求解，本文将原优化问题拆分为三个子问题，即UAV飞行轨迹优化子问题、通信感知调度子问题和雷达感知预编码子问题，并提出一种迭代嵌套方法来求解这些子问题.针对UAV飞行轨迹优化子问题，将其建模为马尔可夫决策过程（Markov Decision Process，MDP），并提出一种基于双重深度

网络的UAV轨迹优化算法.对于给定的MDP系统状态，采用拉格朗日对偶变换方法和二次变换方法求解通信感知调度子问题，并通过引入辅助变量及约束条件等价转换方法求解雷达感知预编码子问题.基于所得到的通信感知调度策略和雷达感知预编码，更新MDP奖励函数，确定UAV飞行轨迹，以迭代实现通信感知调度策略、感知预编码设计及UAV飞行轨迹的联合优化.最后，通过仿真验证了本文所提算法的有效性.

Abstract

In this paper

the scenario of multiple antenna-unmanned aerial vehicle (UAV)-assisted sensing and communication is addressed. Target perception and user communication performance is comprehensively considered

and system cost function is defined as the weighted sum of system energy consumption and user data rate. The optimization problem of communication and sensing scheduling strategy

perception precoding design

and UAV flight trajectory is formulated as a constrained system cost function optimization problem. Due to the highly coupled and non-convex nature of the optimization problem

it is challenging to solve directly. To tackle this problem

the formulated optimization problem is decomposed into three subproblems

i.e.

UAV flight trajectory optimization subproblem

communication and sensing scheduling subproblem

and radar perception precoding subproblem

and an iterative nested method is proposed to solve these subproblems. For UAV flight trajectory optimization subp

roblem

a markov decision process (MDP) is modeled

and a UAV trajectory optimization algorithm is designed based on double deep

-networks. Given the state of the MDP model

the communication and sensing scheduling subproblem is solved using Lagrange dual transformation and quadratic transformation methods

and the radar perception precoding subproblem is addressed through applying equivalent transformation approaches

i.e.

introducing auxiliary variables and converting optimization constraints. Based on the obtained communication and sensing scheduling strategy and radar perception precoding

the reward function of the MDP model is updated and the UAV flight trajectory is determined

so as to achieve the joint optimization of communication and sensing scheduling

perception precoding design

and UAV flight trajectory. The effectiveness of the proposed algorithm is verified through simulations.

关键词

Keywords

references

HASSANIEN A , AMIN M G , ABOUTANIOS E , et al . Dual-function radar communication systems: A solution to the spectrum congestion problem [J ] . IEEE Signal Processing Magazine , 2019 , 36 ( 5 ): 115 - 126 .

WU Q Q , XU J , ZENG Y , et al . A comprehensive overview on 5G-and-beyond networks with UAVs: From communications to sensing and intelligence [J ] . IEEE Journal on Selected Areas in Communications , 2021 , 39 ( 10 ): 2912 - 2945 .

LI X , TAN J W , LIU A F , et al . A novel UAV-enabled data collection scheme for intelligent transportation system through UAV speed control [J ] . IEEE Transactions on Intelligent Transportation Systems , 2021 , 22 ( 4 ): 2100 - 2110 .

QU G X , XIE A L , LIU S Y , et al . Reliable data transmission scheduling for UAV-assisted air-to-ground communications [J ] . IEEE Transactions on Vehicular Technology , 2023 , 72 ( 10 ): 13787 - 13792 .

CUI Y L , DENG D H , WANG C W , et al . Joint trajectory and power optimization for energy efficient UAV communication using deep reinforcement learning [C ] // IEEE INFOCOM 2021-IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS) . Piscataway : IEEE , 2021 : 1 - 6 .

QIN X T , SONG Z Y , HAO Y Y , et al . Joint resource allocation and trajectory optimization for multi-UAV-assisted multi-access mobile edge computing [J ] . IEEE Wireless Communications Letters , 2021 , 10 ( 7 ): 1400 - 1404 .

DAI X H , DUO B , YUAN X J , et al . Energy-efficient UAV communications: A generalized propulsion energy consumption model [J ] . IEEE Wireless Communications Letters , 2022 , 11 ( 10 ): 2150 - 2154 .

RABAHI F Z , BOUDJIT S , BEMMOUSSAT C , et al . UAVs-based mobile radars for real-time highways surveillance [C ] // 2020 IEEE 17th International Conference on Mobile Ad Hoc and Sensor Systems (MASS) . Piscataway : IEEE , 2020 : 80 - 87 .

SUN Z C , WU J J , YEN G G , et al . Performance analysis and system implementation for energy-efficient passive UAV radar imaging system [J ] . IEEE Transactions on Vehicular Technology , 2023 , 72 ( 8 ): 9938 - 9955 .

SAVKIN A V , NI W , ESKANDARI M . Effective UAV navigation for cellular-assisted radio sensing, imaging, and tracking [J ] . IEEE Transactions on Vehicular Technology , 2023 , 72 ( 10 ): 13729 - 13733 .

HUNG H A , HSU H H , CHENG T H . Optimal sensing for tracking task by heterogeneous multi-UAV systems [J ] . IEEE Transactions on Control Systems Technology , 2024 , 32 ( 1 ): 282 - 289 .

CHEN X , FENG Z Y , WEI Z Q , et al . Performance of joint sensing-communication cooperative sensing UAV network [J ] . IEEE Transactions on Vehicular Technology , 2020 , 69 ( 12 ): 15545 - 15556 .

WANG Z J , LIU R K , LIU Q R , et al . QoS-oriented sensing-communication-control co-design for UAV-enabled positioning [J ] . IEEE Transactions on Green Communications and Networking , 2023 , 7 ( 1 ): 497 - 511 .

LIU X , LIU Y M , LIU Z C , et al . Fair integrated sensing and communication for multi-UAV-enabled Internet of Things: Joint 3-D trajectory and resource optimization [J ] . IEEE Internet of Things Journal , 2024 , 11 ( 18 ): 29546 - 29556 .

MENG K T , WU Q Q , MA S D , et al . Throughput maximization for UAV-enabled integrated periodic sensing and communication [J ] . IEEE Transactions on Wireless Communications , 2023 , 22 ( 1 ): 671 - 687 .

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