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

2022/06/20 by Qi Chang, Yanxiang Jiang, Chang, Qi +7
Business, Management and Accounting · Computer Science · #Caching and Content Delivery #Cooperative Communication and Network Coding #FOS: Computer and information sciences #Multiagent Systems (cs.MA) #Sharing Economy and Platforms

paper · pdf · doi:10.48550/arxiv.2206.09549

openalex publication_date 2022/06/20 · openalex created_date 2022/12/14 · openalex updated_date 2026/07/28

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 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 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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