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- W4283313650 abstract "In this paper, the cooperative edge caching problem in fog radio access networks (F-RANs) is investigated. To minimize the content transmission delay, we formulate the cooperative caching optimization problem to find the globally optimal caching strategy. By considering the non-deterministic polynomial hard (NP-hard) property of this problem, a Multi Agent Reinforcement Learning (MARL)-based cooperative caching scheme is proposed. Our proposed scheme applies a double deep Q-network (DDQN) in every fog access point (F-AP), and introduces the communication process in a multi-agent system. Every F-AP records the historical caching strategies of its associated F-APs as the observations of communication procedure. By exchanging the observations, F-APs can leverage the cooperation and make the globally optimal caching strategy. Simulation results show that the proposed MARL-based cooperative caching scheme has remarkable performance compared with the benchmark schemes in minimizing the content transmission delay." @default.
- W4283313650 created "2022-06-24" @default.
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- W4283313650 date "2022-05-16" @default.
- W4283313650 modified "2023-09-26" @default.
- W4283313650 title "Cooperative Edge Caching via Multi Agent Reinforcement Learning in Fog Radio Access Networks" @default.
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- W4283313650 doi "https://doi.org/10.1109/icc45855.2022.9838588" @default.
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