基于DDPG的无人机轨迹规划及功率控制算法  

Unmanned Aerial Vehicle Trajectory Planning and Power Control Algorithm Based on Deep Deterministic Policy Gradient

作　　者：杨青青[1,2] 陈剑 彭艺 YANG Qingqing;CHEN Jian;PENG Yi(Faculty of Information Engineering and Automation,Kunming University of Science and Technology,Kunming 650500,China;Yunnan Key Laboratory of Computer Technologies Application,Kunming University of Science and Technology,Kunming 650500,China)

机构地区：[1]昆明理工大学信息工程与自动化学院,昆明650500 [2]昆明理工大学云南省计算机技术应用重点实验室,昆明650500

出　　处：《北京邮电大学学报》2023年第3期43-48,共6页Journal of Beijing University of Posts and Telecommunications

基　　金：云南省计算机技术应用重点实验室开发基金资助项目(2021102)。

摘　　要：针对无人机辅助地面用户下行通信的场景,以用户的最小平均可达速率最大化为目标,提出了无人机轨迹约束、功率约束和用户接入调度的优化问题。考虑到约束条件的耦合性和优化问题的非凸性,将构建的优化问题建模为马可科夫决策过程,提出了一种基于深度确定性策略梯度(DDPG)的无人机轨迹规划和功率控制算法。仿真结果表明,所提算法能够有效地提高用户的最小平均可达速率。Aiming at the scenario where unmanned aerial vehicles assist users on the ground to carry out downlink communication,the optimization problem of unmanned aerial vehicle trajectory constraint,power constraint and user access scheduling is established with the objective of maximizing the minimum average reachable rate of users.Considering the coupling of the constraints and the non⁃convexity of the optimization problem,the constructed optimization problem is modeled as a Markov decision process,and a trajectory planning and power control algorithm of unmanned aerial vehicle⁃based on deep deterministic policy gradient(DDPG)is proposed.Simulation results show that the proposed algorithm can effectively improve the minimum average achievable rate of users.

关 键 词：无人机 轨迹规划 功率控制 马尔可夫决策过程 深度确定性策略梯度 

分 类 号：TN929.52[电子电信—通信与信息系统]