Learning-based Caching in Cloud-Aided Wireless Networks

TL;DR

This paper introduces a learning-based caching strategy for cloud-assisted wireless networks, optimizing content placement to reduce service delay by leveraging traffic demand patterns and content clustering.

Contribution

It presents a novel cache update algorithm utilizing regret learning and content clustering to adapt to traffic demands in wireless networks.

Findings

Achieves 15% delay reduction in sparse environments.

Achieves 40% delay reduction in dense environments.

Effectively balances global and local content popularity.

Abstract

This paper studies content caching in cloud-aided wireless networks where small cell base stations with limited storage are connected to the cloud via limited capacity fronthaul links. By formulating a utility (inverse of service delay) maximization problem, we propose a cache update algorithm based on spatio-temporal traffic demands. To account for the large number of contents, we propose a content clustering algorithm to group similar contents. Subsequently, with the aid of regret learning at small cell base stations and the cloud, each base station caches contents based on the learned content popularity subject to its storage constraints. The performance of the proposed caching algorithm is evaluated for sparse and dense environments while investigating the tradeoff between global and local class popularity. Simulation results show 15% and 40% gains in the proposed method compared to various baselines.