Content-Aware User Clustering and Caching in Wireless Small Cell Networks

TL;DR

This paper introduces a content-aware user clustering and caching strategy in wireless small cell networks that significantly reduces service delay and enhances offloading efficiency by leveraging user content preferences.

Contribution

It proposes a novel clustering and reinforcement learning-based caching scheme that adapts to user content preferences, outperforming traditional methods.

Findings

Service delay reduced by up to 42% and 27%.

Offloading gain increased by up to 280% and 90%.

Effective content-aware user grouping improves caching performance.

Abstract

In this paper, the problem of content-aware user clustering and content caching in wireless small cell networks is studied. In particular, a service delay minimization problem is formulated, aiming at optimally caching contents at the small cell base stations (SCBSs). To solve the optimization problem, we decouple it into two interrelated subproblems. First, a clustering algorithm is proposed grouping users with similar content popularity to associate similar users to the same SCBS, when possible. Second, a reinforcement learning algorithm is proposed to enable each SCBS to learn the popularity distribution of contents requested by its group of users and optimize its caching strategy accordingly. Simulation results show that by correlating the different popularity patterns of different users, the proposed scheme is able to minimize the service delay by 42% and 27%, while achieving a higher offloading gain of up to 280% and 90%, respectively, compared to random caching and unclustered learning schemes.