# Distributed Edge Caching via Reinforcement Learning in Fog Radio Access Networks

arXiv: https://arxiv.org/abs/1902.10574 · HTML: https://mlsanity.com/paper/1902.10574/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Feb 2019.

## Built on by (most cited first, 3 of 3)

- [A Survey on Reinforcement Learning-Aided Caching in Mobile Edge Networks](https://mlsanity.com/paper/2105.05564/) (2021)
- [Content Popularity Prediction in Fog-RANs: A Clustered Federated Learning Based Approach](https://mlsanity.com/paper/2206.05894/) (2022)
- [A Federated Reinforcement Learning Method with Quantization for Cooperative Edge Caching in Fog Radio Access Networks](https://mlsanity.com/paper/2206.11556/) (2022)

## Abstract (arXiv)

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.