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• W3134103048 abstract "Unmanned-aerial-vehicle (UAV)-assisted communications has attracted increasing attention recently. This article investigates air–ground coordinated communications system, in which trajectories of air UAV base stations (UAV-BSs) and access control of ground users (GUs) are jointly optimized. We formulated this optimization problem as a mixed cooperative–competitive game, where each GU competes for the limited resources of UAV-BSs to maximize its own throughput by accessing a suitable UAV-BS, and UAV-BSs cooperate with each other and design their trajectories to maximize the defined <italic xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>fair throughput</i> to improve the total throughput and keep the GU fairness. Moreover, the action space of GUs is discrete, while that of UAV-BS is continuous. To tackle this hybrid action space issue, we transform the discrete actions into continuous action probabilities and propose a multiagent deep reinforcement learning (MADRL) approach, named air–ground probabilistic multiagent deep deterministic policy gradient (AG-PMADDPG). With well-designed rewards, AG-PMADDPG can coordinate two types of agents, UAV-BSs and GUs, to achieve their own objectives based on local observations. Simulation results demonstrate that AG-PMADDPG can outperform the benchmark algorithms in terms of throughput and fairness." @default.
• W3134103048 created "2021-03-15" @default.
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• W3134103048 date "2022-04-15" @default.
• W3134103048 modified "2023-10-16" @default.
• W3134103048 title "Trajectory Design and Access Control for Air–Ground Coordinated Communications System With Multiagent Deep Reinforcement Learning" @default.
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• W3134103048 doi "https://doi.org/10.1109/jiot.2021.3062091" @default.
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