首页|空地协同通信感知一体化系统的轨迹与资源分配联合优化

空地协同通信感知一体化系统的轨迹与资源分配联合优化

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该文研究空地协同通信感知一体化系统,其中无人车(UGV)基站和无人机(UAV)中继集群组成空地协同网络,为用户提供通信服务,同时对目标区域进行探测感知.在更加准确的莱斯衰落信道模型下,研究联合优化无人机集群的通信感知关联、发射功率和飞行轨迹以及无人车基站的发射功率和行进轨迹,在目标区域感知频率和有效感知功率阈值的约束下,最大化用户最小平均通信速率.为了解决变量高度耦合且非凸的整数优化问题,首先利用块坐标下降法将原问题分解成4个子问题;接着引入松弛变量并将整数约束转化为惩罚项,然后证明莱斯信道下的有效感知功率是关于轨迹变量和松弛变量凸复合函数的联合凸函数;再利用连续凸优化法处理非凸项,并提出一种双层迭代算法高效求解次优解.仿真结果表明,与几种基准方案相比,所提优化算法在相同感知性能下,提高了用户最小平均通信速率,更好地实现了通信与感知性能之间的权衡,并具有良好的收敛性.
Joint Trajectory and Resource Allocation Optimization for Air-ground Collaborative Integrated Sensing and Communication Systems
An air-ground collaborative integrated sensing and communication system is studied,where the air-ground collaborative network is composed of an Unmanned Ground Vehicle(UGV)base station and Unmanned Aerial Vehicle(UAV)relays.The network provides communication service for ground users while detecting and sensing target areas.The air-ground channels are modeled as the accurate Rician fading channel model.On the constraints of the sensing frequency and the effective sensing power threshold of the target areas,the minimum average communication rate of all users is maximized by jointly optimizing the communication and sensing association of the system,the transmit power and flight trajectory of the UAV relays,as well as the transmit power and trajectory of the UGV base station.To solve the resultant non-convex integer optimization problem with highly coupled variables,the block coordinate descent method is applied to decompose the original optimization problem into four sub-problems,where relaxation variables are introduced,and the integer constraints are converted into penalty terms.Then,it is proved that the effective sensing power is a composition function of the trajectory variables and the relaxation variables and is a jointly convex function of them,so that the non-convex terms are tackled by using the successive convex optimization method.Lastly,a two-layer iterative algorithm is proposed to obtain the suboptimal solution efficiently.It is shown by simulation results that compared to some benchmark algorithms,the proposed algorithm significantly increases the minimum average communication rate of all users while achieving the same sensing performance and achieves a better performance trade-off between communication and sensing with good convergence performance.

Integrated sensing and communicationAir-ground collaborationResource allocationTrajectory optimization

张广驰、顾泽霖、崔苗

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广东工业大学信息工程学院 广州 510006

通信感知一体化 空地协同 资源分配 轨迹优化

广东省海洋经济发展项目广东省科技计划广东省科技计划广东省科技计划广东省基础与应用基础研究基金

粤自然资合[2023]24号2023A05050501272022A05050500232022A05050200082023A1515011980

2024

10.11999/JEIT230716

电子与信息学报
中国科学院电子学研究所 国家自然科学基金委员会信息科学部

电子与信息学报

CSTPCD北大核心
影响因子：1.302
ISSN：1009-5896
年,卷(期)：2024.46(6)