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Link to original content: https://doi.org/10.48550/arXiv.2206.09549
[2206.09549] Cooperative Edge Caching via Multi Agent Reinforcement Learning in Fog Radio Access Networks
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Computer Science > Multiagent Systems

arXiv:2206.09549 (cs)
[Submitted on 20 Jun 2022]

Title:Cooperative Edge Caching via Multi Agent Reinforcement Learning in Fog Radio Access Networks

Authors:Qi Chang, Yanxiang Jiang, Fu-Chun Zheng, Mehdi Bennis, Xiaohu You
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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 this http URL considering the non-deterministic polynomial hard (NP-hard) property of this problem, a Multi Agent Reinforcement Learning (MARL)-based cooperative caching scheme is this http URL proposed scheme applies double deep Q-network (DDQN) in every fog access point (F-AP), and introduces the communication process in multi-agent system. Every F-AP records the historical caching strategies of its associated F-APs as the observations of communication this http URL exchanging the observations, F-APs can leverage the cooperation and make the globally optimal caching this http URL results show that the proposed MARL-based cooperative caching scheme has remarkable performance compared with the benchmark schemes in minimizing the content transmission delay.
Comments: This paper has been accepted by IEEE ICC 2022
Subjects: Multiagent Systems (cs.MA)
Cite as: arXiv:2206.09549 [cs.MA]
  (or arXiv:2206.09549v1 [cs.MA] for this version)
  https://doi.org/10.48550/arXiv.2206.09549

Submission history

From: Qi Chang [view email]
[v1] Mon, 20 Jun 2022 03:16:17 UTC (6,222 KB)
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