北京邮电大学学报

  • EI核心期刊

北京邮电大学学报 ›› 2023, Vol. 46 ›› Issue (3): 43-48.

• 人工智能使能网络通信 • 上一篇    下一篇

基于DDPG的无人机轨迹规划及功率控制算法

杨青青1,2, 陈 剑1, 彭 艺1,2   

  1. 1. 昆明理工大学 信息工程与自动化学院 2. 昆明理工大学 云南省计算机技术应用重点实验室
  • 收稿日期:2022-08-19 修回日期:2022-10-10 出版日期:2023-06-28 发布日期:2023-06-05
  • 通讯作者: 彭艺 E-mail:13078770200@163.com
  • 基金资助:
    云南省计算机技术应用重点实验室开发基金资助项目(2021102)

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

YANG Qingqing1,2, CHEN Jian1, PENG Yi1,2   

  • Received:2022-08-19 Revised:2022-10-10 Online:2023-06-28 Published:2023-06-05

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

关键词:

Abstract:

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.

Key words: unmanned aerial vehicle, trajectory planning, power control, Markov decision process;deep deterministic policy gradient

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