User Preference Learning Based Edge Caching for Fog Radio Access Network

TL;DR

This paper introduces a novel online and offline learning-based edge caching policy for fog radio access networks that predicts content popularity dynamically, improving cache hit rates and approaching optimal performance.

Contribution

It proposes a new edge caching strategy combining online and offline learning algorithms to adapt to spatial and temporal content popularity variations.

Findings

Achieves higher cache hit rates than traditional policies.

Effectively tracks dynamic content popularity without delay.

Theoretically bounds prediction error and cache performance.

Abstract

In this paper, the edge caching problem in fog radio access network (F-RAN) is investigated. By maximizing the overall cache hit rate, the edge caching optimization problem is formulated to find the optimal policy. Content popularity in terms of time and space is considered from the perspective of regional users. We propose an online content popularity prediction algorithm by leveraging the content features and user preferences, and an offline user preference learning algorithm by using the {online gradient descent} (OGD) method and the {follow the (proximally) regularized leader} (FTRL-Proximal) method. Our proposed edge caching policy not only can promptly predict the future content popularity in an online fashion with low complexity, {but also} can track the content popularity with spatial and temporal popularity dynamic in time without delay. Furthermore, we design two learning based edge caching architectures. Moreover, we theoretically derive the upper bound of the popularity prediction error, the lower bound of the cache hit rate, and the regret bound of the overall cache hit rate of our proposed edge caching policy. Simulation results show that the overall cache hit rate of our proposed policy is superior to those of the traditional policies and asymptotically approaches the optimal performance.