面向用户移动场景的无人机中继功率分配与轨迹设计

Power Allocation and Trajectory Design for Unmanned Aerial Vehicle Relay Network with Mobile Users

在无人机(UAV)中继通信中,中继无人机的通信资源分配与运动规划是需要重点解决的问题。为了提升无人机中继通信系统的通信效率,该文提出一种基于近端策略优化算法的无人机中继功率分配与轨迹设计联合规划方法。该方法将用户移动场景下无人机中继功率分配与轨迹设计联合规划问题建模为马尔可夫决策过程,考虑用户位置信息获取不精确的情形,在满足用户中断概率约束的前提下,以中继通信系统的吞吐量最大为优化目标设置奖励函数,采用一种收敛速度较快的深度强化学习算法——近端策略优化算(PPO)法求解,实现中继无人机飞行轨迹优化和中继发射功率合理有效分配。仿真实验结果表明,针对用户随机移动的无人机中继通信场景,该文所提方法与基于随机策略和传统深度确定性策略梯度(DDPG)的方法相比,系统吞吐量分别提升22%和15%。结果表明,所提方法能够有效地提高系统的通信效率。

In Unmanned Aerial Vehicle(UAV)relay networks,communication resource allocation and motion planning of UAV are the key problems that should be solved.In order to improve the communication efficiency of UAV relay communication system,a joint planning method of UAV relay power allocation and trajectory design is proposed based on proximal policy optimization algorithm.The joint planning problem of UAV relay power allocation and trajectory design in the user movement scenario is modelled as a Markov decision-making process.Considering the inaccurate acquisition of user location information,the reward function is set with the maximum throughput of the relay communication system as the optimization goal under the premise of satisfying the user interruption probability constraint.Then,a deep reinforcement learning algorithm with high convergence speed—the Proximal Policy Optimization(PPO)algorithm,is used to solve the problem and realized the flight trajectory optimization of relay UAV and the reasonable and effective allocation of relay transmission power.The simulation experimental results show that for the scenario of UAV relay communication with random users movement,the proposed method improves system throughput by 22%and 15%,respectively,compared to the methods based on random strategy and traditional Deep Deterministic Policy Gradient(DDPG).The results show that the proposed method can effectively improve the communication efficiency of the system.