基于GRU-MAPPO的应急通信无人机轨迹优化与功率分配OA

In post-disaster scenarios with damaged terrestrial communication infrastructure,unmanned aerial vehicles(UAVs)can be rapidly deployed as aerial base stations to establish a temporary emergency communication network.However,the distribution of users in disaster areas is often significantly uneven,and the coverage capability of a single UAV is limited.This poses challenges for multi-agent systems to collaboratively ensure service quality in dynamic environments.To address this issue,a joint trajectory and resource optimization framework based on gated recurrent unit multi-agent proximal policy optimization(GRU-MAPPO)was proposed.In this framework,a GRU module was introduced into the process of policy learning,enabling UAVs to adaptively adapt their flight trajectories and transmit power under partial observable conditions,thereby improving system throughput and energy efficiency.To address the slow training convergence and low sample efficiency issues caused by the high-dimensional state space and continuous action space,a hybrid reward function was designed and combined with a linear curriculum learning scheduling strategy to improve the training process and facilitate convergence.Simulation results show that the proposed algorithm achieves better convergence stability and outperforms benchmark algorithms in terms of throughput and energy efficiency,providing enhanced reliability and environmental adaptability of communication systems in complex post-disaster rescue scenarios.