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- W4386654818 abstract "In this chapter, we investigate multi-dimensional resource management for UAV-assisted MVNETs. To efficiently provide on-demand resource access, the MeNB and UAV, both mounted with MEC servers, cooperatively make association decisions and allocate proper amounts of resources to vehicles. First, we introduce an SADDPG-based scheme to centrally allocate the multi-dimensional resources by considering a central controller installed at the MeNB. Also, to avoid extra time and spectrum consumption on communications between MEC servers and a central controller, we formulate the resource allocation at the MEC servers as a distributive optimization problem with the objective of maximizing the number of offloaded tasks while satisfying their heterogeneous QoS requirements. Then, we solve the formulated distributive problem with an MADDPG-based method. Through centrally training the MADDPG model offline, the MEC servers, acting as learning agents, can rapidly make vehicle-server association and resource allocation decisions during the online execution stage. From our simulation results, the MADDPG-based method can converge within 200 training episodes, comparable to the SADDPG-based one. Moreover, the proposed SADDPG-based and MADDPG-based resource management scheme can achieve higher delay/QoS satisfaction ratios than the random scheme." @default.
- W4386654818 created "2023-09-13" @default.
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- W4386654818 date "2012-02-24" @default.
- W4386654818 modified "2023-09-26" @default.
- W4386654818 title "Aerial-Assisted Intelligent Resource Allocation" @default.
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- W4386654818 doi "https://doi.org/10.1007/978-3-030-96507-5_5" @default.
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