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您的检索式：作者名="Xuanhan ZHOU"
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| 1 | Joint UAV trajectory and communication design with heterogeneous multi-agent reinforcement learning显示文摘Unmanned aerial vehicles(UAVs)are recognized as effective means for delivering emergency communication services when terrestrial infrastructures are unavailable.This paper investigates a multiUAV-assisted communication system,where we jointly optimize UAVs'trajectories,user association,and ground users(GUs)'transmit power to maximize a defined fairness-weighted throughput metric.Owing to the dynamic nature of UAVs,this problem has to be solved in real time.However,the problem's non-convex and combinatorial attributes pose challenges for conventional optimization-based algorithms,particularly in scenarios without central controllers.To address this issue,we propose a multi-agent deep reinforcement learning(MADRL)approach to provide distributed and online solutions.In contrast to previous MADRLbased methods considering only UAV agents,we model UAVs and GUs as heterogeneous agents sharing a common objective.Specifically,UAVs are tasked with optimizing their trajectories,while GUs are responsible for selecting a UAV for association and determining a transmit power level.To learn policies for these heterogeneous agents,we design a heterogeneous coordinated QMIX(HC-QMIX)algorithm to train local Q-networks in a centralized manner.With these well-trained local Q-networks,UAVs and GUs can make individual decisions based on their local observations.Extensive simulation results demonstrate that the proposed algorithm outperforms state-of-the-art benchmarks in terms of total throughput and system fairness. | Xuanhan ZHOU Jun XIONG Haitao ZHAO Xiaoran LIU Baoquan REN Xiaochen ZHANG Jibo WEI Hao YIN | 2024 | Science China(Information Sciences)2024，67，3： | 0 |
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