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

TL;DR

This paper proposes a multi-agent reinforcement learning approach using DDQN for cooperative edge caching in fog radio access networks, significantly reducing content transmission delay through optimized strategies.

Contribution

It introduces a novel MARL-based cooperative caching scheme with communication among F-APs, addressing NP-hard optimization in F-RANs.

Findings

Significant delay reduction compared to benchmarks

Effective cooperation among F-APs via observation exchange

Robust performance in diverse network scenarios

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.