基于MADDPG的无人机辅助通信功率分配算法

针对多无人机(unmanned aerial vehicle, UAV)作为空中基站辅助通信的吞吐量和公平性问题，提出了一种基于多智能体深度确定性策略梯度算法(multi-agent deep deterministic policy gradient algorithms, MADDPG)的功率分配算法，该算法通过联合优化UAV基站的功率分配和用户接入以提高系统吞吐量和公平性。本文首先构建了UAV基站为地面建立通信服务的三维场景，然后通过联合功率、用户关联和UAV位置约束，构建了吞吐量和公平性最大化的问题模型。考虑到该问题的复杂性，本文将所构建的优化问题建模为马尔科夫决策过程(Markov decision process, MDP),通过引入深度确定性策略梯度算法(deep deterministic policy gradient algorithm, DDPG)解决该问题。仿真结果表明，本文提出的基于MADDPG的UAV基站功率分配算法与其他算法相比，可以有效地提升系统的吞吐量和用户的公平性，提高通信的服务质量。

Algorithm of UAV auxiliary communication power allocation based on multi-agent deep deterministic policy gradient algorithms

Aiming at throughput and fairness issues for multi-unmanned aerial vehicle (multi-UAV) as aerial base station auxiliary communication,a power allocation algorithm based on the multi-agent deep deterministic policy gradient (MADDPG) algorithm is proposed.The algorithm improves system throughput and fairness by jointly optimizing the power allocation and user access of UAV base stations.This paper firstly constructs a 3D scene in which the UAV base station establishes communication services for the ground and then constructs a problem model of maximizing throughput and fairness by combining jonit power,user association,and UAV location constraints.Considering the complexity of the problem,this paper models the constructed optimization problem as a Markov decision process (MDP),and solves the problem by MADDPG.The simulation results show that compared with other algorithms,the UAV base station power allocation algorithm based on MADDPG proposed in this paper can effectively improve the throughput of the system and the fairness of users,to improve the service quality of communication.