Cooperative edge caching via multi agent reinforcement learning in fog radio access networks
Chang, Qi; Jiang, Yanxiang; Zheng, Fu-Chun; Bennis, Mehdi; You, Xiaohu (2022-08-11)
Q. Chang, Y. Jiang, F. -C. Zheng, M. Bennis and X. You, "Cooperative Edge Caching via Multi Agent Reinforcement Learning in Fog Radio Access Networks," ICC 2022 - IEEE International Conference on Communications, Seoul, Korea, Republic of, 2022, pp. 3641-3646, doi: 10.1109/ICC45855.2022.9838588
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https://urn.fi/URN:NBN:fi-fe2023021026809
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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.
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