Distributed Edge Caching via Reinforcement Learning in Fog Radio Access Networks

TL;DR

This paper introduces a distributed reinforcement learning approach for edge caching in fog radio access networks, effectively adapting to dynamic content popularity and user preferences without additional communication overhead.

Contribution

It proposes a novel Q-learning based framework with value function approximation for distributed cache optimization in F-RANs, improving efficiency and convergence.

Findings

Outperforms traditional caching methods in simulations.

Reduces complexity and accelerates convergence with Q-VFA-learning.

Effectively adapts to dynamic spatio-temporal traffic demands.

Abstract

In this paper, the distributed edge caching problem in fog radio access networks (F-RANs) is investigated. By considering the unknown spatio-temporal content popularity and user preference, a user request model based on hidden Markov process is proposed to characterize the fluctuant spatio-temporal traffic demands in F-RANs. Then, the Q-learning method based on the reinforcement learning (RL) framework is put forth to seek the optimal caching policy in a distributed manner, which enables fog access points (F-APs) to learn and track the potential dynamic process without extra communications cost. Furthermore, we propose a more efficient Q-learning method with value function approximation (Q-VFA-learning) to reduce complexity and accelerate convergence. Simulation results show that the performance of our proposed method is superior to those of the traditional methods.