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• W4328012117 abstract "Considering a two-tier HetNet adopting orthogonal frequency division multiple access (OFDMA) in downlink, we propose a multi-agent deep reinforcement learning (MA-DRL) scheme to perform channel assignment and power allocation. The goal is to jointly optimize the system capacity and power consumption. In the proposed scheme, the macro base station (MBS) and pico base stations (PBSs) play the role of agents. Each agent consists of a deep <tex xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>$Q$</tex> network (DQN) and a deep deterministic policy gradient (DDPG) network performing channel assignment and power allocation in serial. Simulation results show that the proposed algorithm converges steadily and can be used to improve the system capacity and reduce the power consumption effectively." @default.
• W4328012117 created "2023-03-22" @default.
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• W4328012117 date "2022-12-09" @default.
• W4328012117 modified "2023-09-24" @default.
• W4328012117 title "Multi-objective Resource Allocation Based on Deep Reinforcement Learning in HetNets" @default.
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• W4328012117 doi "https://doi.org/10.1109/iccc56324.2022.10065879" @default.
• W4328012117 hasPublicationYear "2022" @default.
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